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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":2230},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":1045,"featuredImage":1046,"featuredImageAlt":1047,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":1048,"publishedAt":1049,"createdAt":1050,"updatedAt":1051,"seoLocalePaths":1052,"categories":1061,"author":1085,"translations":1090},"480","Kada bi AI trebalo da prestane da veruje sopstvenom znanju? — Okidač za pretragu","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u003Ch2 id=\"section-1\">Pitanje\u003C\u002Fh2>\n\u003Cp>Kada AI treba da prestane da se oslanja na ono što već zna i da pre odgovaranja pribavi spoljne informacije?\u003C\u002Fp>\n\u003Cp>Ovo pitanje izgleda jednostavno, ali se nalazi u središtu jedne od najvažnijih dizajnerskih odluka u savremenim AI sistemima.\u003C\u002Fp>\n\u003Cp>Veliki jezički modeli sadrže značajno znanje u svojim parametrima. Generisanje uz pomoć pretrage dodaje spoljne informacije u vreme izvršavanja. Ali nijedna krajnost nije idealna.\u003C\u002Fp>\n\u003Cp>Stalno verovanje modelu može da proizvede zastarele ili nepotkrepljene odgovore. Stalno pribavljanje informacija dodaje latenciju, troškove, irelevantan kontekst i nove mogućnosti za greške u pretrazi.\u003C\u002Fp>\n\u003Cp>Pravi problem stoga nastupa pre RAG-a: Kada uopšte treba da se izvrši pretraga?\u003C\u002Fp>\n\u003Cp>Ovaj članak koristi termin okidač za pretragu za tu odluku. Okidač za pretragu ovde nije predstavljen kao standardizovan termin iz istraživačke literature. To je praktičan sistemski koncept koji objedinjuje ideje već vidljive u istraživanjima o aktivnoj, adaptivnoj i samorefleksivnoj pretrazi.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Okidač za pretragu je uslov koji ukazuje da AI sistem treba da prestane da se oslanja isključivo na znanje internog modela i da pribavi spoljne dokaze pre nego što proizvede ili finalizuje odgovor.\u003Ccite class=\"block mt-2 text-sm\">— Radna definicija\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-1\" class=\"editorjs-toc__link\">Pitanje\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-10\" class=\"editorjs-toc__link\">Šta to zapravo znači\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-20\" class=\"editorjs-toc__link\">Najjednostavniji primer\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">Gde primer prestaje da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-43\" class=\"editorjs-toc__link\">Direktan odgovor\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-48\" class=\"editorjs-toc__link\">Zašto je to tako\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Kontekst\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Pretpostavke\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-71\" class=\"editorjs-toc__link\">Promenljive\u003C\u002Fa>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-1\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-73\" class=\"editorjs-toc__link\">Svežina\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-75\" class=\"editorjs-toc__link\">Specifičnost\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-77\" class=\"editorjs-toc__link\">Zahtev za dokazima\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-79\" class=\"editorjs-toc__link\">Pokrivenost znanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-81\" class=\"editorjs-toc__link\">Posledica greške\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-84\" class=\"editorjs-toc__link\">Dijagnostička \u002F metoda odlučivanja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-96\" class=\"editorjs-toc__link\">Dokazi\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-104\" class=\"editorjs-toc__link\">Stvarni primeri\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-119\" class=\"editorjs-toc__link\">Uobičajene zablude i načini neuspeha\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-125\" class=\"editorjs-toc__link\">Rubni slučajevi\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-136\" class=\"editorjs-toc__link\">Ograničenja\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-143\" class=\"editorjs-toc__link\">Šta bi promenilo ovaj odgovor?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-149\" class=\"editorjs-toc__link\">Zaključak\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-157\" class=\"editorjs-toc__link\">Primarni izvori\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-10\">Šta to zapravo znači\u003C\u002Fh2>\n\u003Cp>LLM ima dva suštinski različita načina dobijanja informacija.\u003C\u002Fp>\n\u003Cp>Prvi je znanje modela. To su informacije predstavljene u naučenim parametrima modela. Nije potreban upit ka bazi podataka, veb pretraga niti pretraživanje dokumenata u vreme izvršavanja.\u003C\u002Fp>\n\u003Cp>Drugi je znanje u vreme izvršavanja. To su informacije koje se pružaju dok model radi: rezultati pretrage, zapisi iz baze podataka, dokumenti, API-ji, korisnički fajlovi, izlazi alata ili drugi pribavljeni dokazi.\u003C\u002Fp>\n\u003Cp>RAG povezuje ova dva sveta. Ali sam RAG ne odgovara na pitanje kada ta veza treba da se aktivira. Za to služi okidač za pretragu.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Okidač za pretragu stoga nastupa pre pretrage. Granica valjanosti odgovora nastupa kasnije.\u003C\u002Fp>\n\u003Cp>Prvi pita: Da li su mi potrebni spoljni dokazi?\u003C\u002Fp>\n\u003Cp>Drugi pita: Da li sada imam dovoljno dokaza da potkrepim ovaj odgovor?\u003C\u002Fp>\n\u003Cp>Ovo su povezane odluke, ali nisu ista odluka.\u003C\u002Fp>\n\u003Ch2 id=\"section-20\">Najjednostavniji primer\u003C\u002Fh2>\n\u003Cp>Razmotrite tri pitanja.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Pitanje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Interno znanje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Okidač za pretragu\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji je glavni grad Francuske?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Obično dovoljno\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Nema jakog okidača\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koja je trenutna cena akcija NVIDIA?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Potencijalno zastarelo\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Pokreni pretragu\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Da li ovaj novi naučni rad dokazuje da X izaziva Y?\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Ne može se utvrditi tvrdnja bez ispitivanja dokaza\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Jak okidač za pretragu\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Prvo pitanje se zasniva na veoma stabilnoj činjenici.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the capital of France?"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Preuzimanje dokumenata pre odgovaranja obično bi dodalo malo vrednosti.\u003C\u002Fp>\n\u003Cp>Sada razmotrite pitanje čiji se odgovor neprekidno menja.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"What is the current NVIDIA stock price?"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Model može znati mnogo o NVIDIA. To ne znači da zna cenu sada.\u003C\u002Fp>\n\u003Cp>Treći primer je još važniji.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>User\n↓\n"Does this new scientific paper prove that X causes Y?"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Sposobnost zaključivanja modela može biti savršeno korisna. Nedostaje komponenta dokaza.\u003C\u002Fp>\n\u003Cp>Ta razlika je fundamentalna.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">Gde primer prestaje da funkcioniše\u003C\u002Fh2>\n\u003Cp>Gornji primeri čine da odluka izgleda binarno: preuzmi ili ne preuzimaj.\u003C\u002Fp>\n\u003Cp>Stvarni sistemi su složeniji. Pitanje može sadržati nekoliko tvrdnji, neke stabilne, a neke aktuelne. Preuzeti dokumenti mogu se ne slagati. Pretraživač može vratiti irelevantne informacije. Relevantne informacije mogu postojati, ali ne uspeti da se rangiraju dovoljno visoko. Dokument može biti autoritativan, ali zastareo.\u003C\u002Fp>\n\u003Cp>Sama pretraga takođe može uneti netačan kontekst u inače razuman odgovor.\u003C\u002Fp>\n\u003Cp>Zbog toga pretragu ne treba tretirati kao automatski sinonim za istinu.\u003C\u002Fp>\n\u003Cp>Istraživanje o adaptivnoj pretrazi sve se više udaljava od pretpostavke da svaki upit treba da dobije istu strategiju pretrage.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, na primer, eksplicitno istražuje pretragu na zahtev, umesto da neselektivno preuzima fiksni broj odlomaka za svaki unos. Autori raspravljaju o tome kako nepotrebna ili irelevantna pretraga može smanjiti kvalitet odgovora.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> na sličan način bira između pretrage bez pretrage, jednostepene pretrage i složenijih strategija pretrage u zavisnosti od složenosti pitanja.\u003C\u002Fp>\n\u003Cp>Dakle, važno pitanje nije: Da li ovaj sistem ima RAG?\u003C\u002Fp>\n\u003Cp>Već: Može li ovaj sistem da prepozna kada je pretraga neophodna i koja vrsta pretrage je odgovarajuća?\u003C\u002Fp>\n\u003Ch2 id=\"section-43\">Direktan odgovor\u003C\u002Fh2>\n\u003Cp>AI treba da pokrene pretragu kada odgovaranje zahteva informacije koje njegovo interno znanje modela ne može bezbedno da pruži sa potrebnom svežinom, specifičnošću, poreklom ili dokaznom podrškom.\u003C\u002Fp>\n\u003Cp>U praktičnim sistemima, okidač za pretragu može proizaći iz nekoliko uslova:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ako nijedan od ovih uslova nije materijalno prisutan, pretraga može biti nepotrebna. Ako je jedan ili više njih prisutno, spoljni dokazi postaju deo procesa generisanja odgovora.\u003C\u002Fp>\n\u003Ch2 id=\"section-48\">Zašto je to tako\u003C\u002Fh2>\n\u003Cp>Interno znanje jezičkog modela često se opisuje kao parametarsko znanje. Ono je naučeno tokom obuke i kodirano u parametre modela.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Originalni RAG rad Lewisa i saradnika\u003C\u002Fa> predstavio je pretragu kao kombinaciju ove parametarske memorije sa spoljnom, neparametarskom memorijom. Spoljna memorija može se pretraživati i ažurirati bez ponovnog obučavanja celokupnog jezičkog modela.\u003C\u002Fp>\n\u003Cp>Ova razlika stvara neizbežan sistemski problem.\u003C\u002Fp>\n\u003Cp>Model može da zna stvari. Ali model ne može pretpostaviti da je sve što zna aktuelno, potpuno, dovoljno specifično i potkrepljeno potrebnim dokazima.\u003C\u002Fp>\n\u003Cp>Model stoga može proizvesti lingvistički uverljiv odgovor, a da pritom posluje izvan tačke u kojoj je njegovo interno znanje dovoljno.\u003C\u002Fp>\n\u003Cp>Ta tačka je mesto gde okidač za pretragu postaje koristan.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Kontekst\u003C\u002Fh2>\n\u003Cp>Tradicionalni RAG često izgleda ovako:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ova arhitektura pretpostavlja pretragu pre generisanja. To dobro funkcioniše za mnoge aplikacije intenzivne znanjem, ali može izvršiti i nepotrebnu pretragu.\u003C\u002Fp>\n\u003Cp>Napredniji pristupi uvode adaptivni korak:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> ide dalje razmatrajući pretragu tokom samog generisanja. Koristi predstojeće generisanje i tokene niske pouzdanosti kao signale za preuzimanje dodatnih informacija.\u003C\u002Fp>\n\u003Cp>Self-RAG na sličan način uvodi mehanizme koji omogućavaju pretrazi, generisanju i kritici da interaguju umesto da se pretraga tretira kao bezuslovni korak predobrade.\u003C\u002Fp>\n\u003Cp>Adaptive-RAG pristupa istom širem problemu iz ugla složenosti upita: različita pitanja mogu zahtevati različite strategije pretrage.\u003C\u002Fp>\n\u003Cp>Ovi pristupi se tehnički razlikuju. Ali otkrivaju isti arhitektonski uvid: Pretraga bi trebalo da bude odluka, a ne samo trajni prekidač.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Pretpostavke\u003C\u002Fh2>\n\u003Cp>Okvir Retrieval Trigger pretpostavlja da sistem ima pristup najmanje jednom eksternom izvoru informacija kada je pretraga potrebna.\u003C\u002Fp>\n\u003Cp>Taj izvor može biti web pretraga, skladište dokumenata, vektorska baza podataka, SQL baza podataka, graf znanja, API, poslovni sistem, dokument koji je otpremio korisnik ili izlaz alata.\u003C\u002Fp>\n\u003Cp>Takođe pretpostavlja da pretraga ima cenu. Ta cena ne mora biti finansijska.\u003C\u002Fp>\n\u003Cp>Pretraga uvodi latenciju, potrošnju tokena, korišćenje konteksta, složenost infrastrukture i mogućnost preuzimanja obmanjujućih informacija.\u003C\u002Fp>\n\u003Cp>Optimalni sistem stoga ne maksimizuje pretragu. On maksimizuje odgovarajuću pretragu.\u003C\u002Fp>\n\u003Ch2 id=\"section-71\">Promenljive\u003C\u002Fh2>\n\u003Cp>Praktični Retrieval Trigger može razmotriti pet primarnih promenljivih.\u003C\u002Fp>\n\u003Ch3 id=\"section-73\">Svežina\u003C\u002Fh3>\n\u003Cp>Kolika je verovatnoća da su tražene informacije promenjene? Glavni grad Francuske ima veoma nisku volatilnost. Cena akcija ima izuzetno visoku volatilnost.\u003C\u002Fp>\n\u003Ch3 id=\"section-75\">Specifičnost\u003C\u002Fh3>\n\u003Cp>Da li pitanje zahteva informacije iz određenog izvora, dokumenta, organizacije, naloga ili skupa podataka? Ako korisnik pita šta piše u konkretnom ugovoru, opšte znanje modela je irelevantno. Ugovor mora biti pronađen.\u003C\u002Fp>\n\u003Ch3 id=\"section-77\">Zahtev za dokazima\u003C\u002Fh3>\n\u003Cp>Da li odgovor zahteva poreklo? Model može znati da je tvrdnja opšteprihvaćena, ali i dalje može biti potreban izvor kada zadatak zahteva verifikaciju.\u003C\u002Fp>\n\u003Ch3 id=\"section-79\">Pokrivenost znanja\u003C\u002Fh3>\n\u003Cp>Da li je verovatno da je tema adekvatno zastupljena u internom znanju modela? Retke, vlasničke, visoko lokalne ili novoobjavljene informacije stvaraju veći pritisak za pretragu.\u003C\u002Fp>\n\u003Ch3 id=\"section-81\">Posledica greške\u003C\u002Fh3>\n\u003Cp>Nema svaki netačan odgovor isti uticaj. Kada faktografska tačnost materijalno utiče na odluku, prihvatljiv prag dokaza može biti viši.\u003C\u002Fp>\n\u003Cp>Ove promenljive ne moraju biti implementirane kao doslovne numeričke ocene. One opisuju površinu odlučivanja.\u003C\u002Fp>\n\u003Ch2 id=\"section-84\">Dijagnostička \u002F metoda odlučivanja\u003C\u002Fh2>\n\u003Cp>Vrlo jednostavan okidač za pretragu može se implementirati bez mašinskog učenja.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Za stabilno faktografsko pitanje:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve()\n# False\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Za trenutnu cenu akcija:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    time_sensitive=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Za naučnu tvrdnju:\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Proizvodni sistemi mogu ovu odluku učiniti daleko sofisticiranijom. Klasifikator bi mogao da predvidi zahteve za pretragu. Model bi mogao da emituje specijalne kontrolne tokene. Ruter bi mogao da klasifikuje složenost upita. Pretraga bi takođe mogla biti pokrenuta više puta tokom generisanja.\u003C\u002Fp>\n\u003Cp>Implementacija se može promeniti. Arhitektonsko pitanje ostaje isto:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Da li su dokazi trenutno dostupni modelu dovoljni za odgovor koji će proizvesti?\u003C\u002Fblockquote>\n\u003Ch2 id=\"section-96\">Dokazi\u003C\u002Fh2>\n\u003Cp>Koncept predložen ovde je u skladu sa nekoliko pravaca istraživanja pretrage.\u003C\u002Fp>\n\u003Cp>Originalna \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">RAG arhitektura\u003C\u002Fa> pokazala je korisnost kombinovanja parametarskog znanja modela sa eksternim neparametarskim znanjem, posebno za zadatke intenzivne znanjem.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> eksplicitno istražuje aktivnu pretragu tokom generisanja, uključujući pretragu podstaknutu sadržajem koji sledi sa niskom pouzdanošću.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demonstrira arhitekturu u kojoj se pretraga može obaviti na zahtev i nakon koje sledi refleksija o pronađenim odlomcima i generisanom sadržaju.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> dinamički bira između različitih strategija u zavisnosti od složenosti pitanja, uključujući situacije kada pretraga nije potrebna.\u003C\u002Fp>\n\u003Cp>Termin Retrieval Trigger se ovde koristi kao apstrakcija na nivou sistema nad ovom širom familijom odluka.\u003C\u002Fp>\n\u003Cp>Ne tvrdi se da ovi radovi koriste istu terminologiju. Umesto toga, identifikuje se zajednički arhitektonski problem: Šta uzrokuje da AI sistem pređe sa internog znanja na eksterne dokaze?\u003C\u002Fp>\n\u003Ch2 id=\"section-104\">Stvarni primeri\u003C\u002Fh2>\n\u003Cp>Razmotrite asistenta za podršku povezanog sa dokumentacijom kompanije.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"How do I reset my password?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ako je procedura stabilna i pouzdano predstavljena u trenutnim instrukcijama asistenta, direktno odgovaranje može biti prikladno.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What permissions does my account currently have?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ta informacija je specifična za korisnika i dinamična. Okidač za preuzimanje se aktivira. Sistem mora da pregleda stvarne podatke o nalogu ili autorizaciji.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Why was my production deployment rejected yesterday?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Model može da razume sisteme za raspoređivanje i objasni uobičajene razloge. Ali pitanje se odnosi na konkretan događaj. Potrebni su dnevnici, CI\u002FCD izlaz ili zapisi o incidentima.\u003C\u002Fp>\n\u003Cp>Ista logika važi i za web pretragu.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is RAG?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Opšte objašnjenje možda ne zahteva preuzimanje.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Sada su potrebni dokazi specifični za izvor.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"What is the latest research on adaptive retrieval?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ovo uvodi i zahtev za svežinom. Osnovna tema se nije promenila. Zahtev za informacijom jeste.\u003C\u002Fp>\n\u003Ch2 id=\"section-119\">Uobičajene zablude i načini neuspeha\u003C\u002Fh2>\n\u003Cp>Više preuzimanja automatski proizvodi bolji odgovor. Ne. Irelevantni dokumenti troše kontekst i mogu da odvrate generisanje.\u003C\u002Fp>\n\u003Cp>Visoka pouzdanost modela znači da preuzimanje nije potrebno. Model može samouvereno da proizvede netačan odgovor. Samoprijavljena pouzdanost stoga ne treba da se tretira kao jedini okidač.\u003C\u002Fp>\n\u003Cp>Uspešno preuzimanje znači da je odgovor verifikovan. Preuzimanje samo pruža kandidatske dokaze. Dokazi i dalje moraju biti relevantni, dovoljno autoritativni i ispravno interpretirani.\u003C\u002Fp>\n\u003Cp>RAG automatski rešava zastarelo znanje. To čini samo ako sam korpus za preuzimanje sadrži ažurne informacije. Preuzimanje zastarelog dokumenta ne stvara ažuran odgovor.\u003C\u002Fp>\n\u003Cp>Jedan korak preuzimanja je uvek dovoljan. Složena pitanja mogu zahtevati nekoliko dokaza ili iterativno preuzimanje.\u003C\u002Fp>\n\u003Ch2 id=\"section-125\">Rubni slučajevi\u003C\u002Fh2>\n\u003Cp>Neka pitanja sadrže i stabilne i nestabilne informacije.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>"Who founded NVIDIA, and what is its market capitalization today?"\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Prvi deo možda može da se odgovori na osnovu stabilnog znanja modela. Drugi deo zahteva aktuelne informacije.\u003C\u002Fp>\n\u003Cp>Dovoljno sposoban sistem ne bi trebalo nužno da tretira ceo upit kao jednu odluku o pretraživanju. Može da pokrene pretraživanje samo tamo gde je potrebno.\u003C\u002Fp>\n\u003Cp>Još jedan granični slučaj je neslaganje između izvora. Pretpostavimo da pretraživanje vrati tri dokumenta koji daju nekompatibilne tvrdnje.\u003C\u002Fp>\n\u003Cp>Okidač za pretraživanje je već uspeo: sistem je prepoznao da su spoljni dokazi bili potrebni. Ali zadatak nije završen.\u003C\u002Fp>\n\u003Cp>Sistem je sada došao do problema procene dokaza. Tu granica valjanosti odgovora postaje važna.\u003C\u002Fp>\n\u003Cp>Sistem je možda preuzeo informacije i još uvek ne poseduje dovoljno dokaza da donese čvrst zaključak.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Retrieval Trigger\n≠\npermission to answer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Okidač pribavlja dokaze. Granica valjanosti određuje da li su ti dokazi dovoljni.\u003C\u002Fp>\n\u003Ch2 id=\"section-136\">Ograničenja\u003C\u002Fh2>\n\u003Cp>Okidač za pretraživanje je konceptualni okvir, a ne univerzalni algoritam.\u003C\u002Fp>\n\u003Cp>Različiti sistemi će zahtevati različita pravila okidanja. Bot za korisničku podršku, asistent za naučna istraživanja, pretraživač i autonomni softverski agent nemaju identične zahteve za dokazima.\u003C\u002Fp>\n\u003Cp>Pragovi okidanja takođe mogu da stvore sopstvene načine neuspeha. Prag koji je prenizak izaziva prekomerno pretraživanje. Prag koji je previsok izaziva nepotkrepljeno odgovaranje.\u003C\u002Fp>\n\u003Cp>Sama infrastruktura za pretraživanje je takođe važna. Savršen okidač povezan sa lošom kolekcijom izvora i dalje proizvodi loše dokaze.\u003C\u002Fp>\n\u003Cp>Slično tome, odlična baza znanja pruža malu vrednost ako se okidač nikada ne aktivira kada je potreban.\u003C\u002Fp>\n\u003Cp>Okidač za pretraživanje stoga rešava samo jedan deo veće arhitekture.\u003C\u002Fp>\n\u003Ch2 id=\"section-143\">Šta bi promenilo ovaj odgovor?\u003C\u002Fh2>\n\u003Cp>Budući modeli mogu da sadrže bolje mehanizme za identifikovanje sopstvenih ograničenja znanja. Pretraživači mogu postati jeftiniji i brži. Sistemi sa dugim kontekstom mogu neprekidno da nose mnogo više izvornog materijala.\u003C\u002Fp>\n\u003Cp>Modeli takođe mogu sve više kombinovati pretragu, baze podataka, alate i strukturisano znanje bez izlaganja posebne RAG faze programeru aplikacije.\u003C\u002Fp>\n\u003Cp>Ove promene mogu izmeniti način na koji se trigger implementira. One ne uklanjaju nužno osnovnu odluku.\u003C\u002Fp>\n\u003Cp>Sve dok postoji razlika između informacija koje su modelu već dostupne i informacija koje se moraju pribaviti spolja, sistemu je i dalje potreban neki mehanizam za određivanje kada da pređe tu granicu.\u003C\u002Fp>\n\u003Cp>Implementacija može nestati iz vida. Arhitektonsko pitanje ostaje.\u003C\u002Fp>\n\u003Ch2 id=\"section-149\">Zaključak\u003C\u002Fh2>\n\u003Cp>RAG počinje previše kasno da bi objasnio ceo problem.\u003C\u002Fp>\n\u003Cp>Pre nego što pretraga može da se dogodi, AI sistem mora da utvrdi da li je pretraga neophodna. Ta odluka je Retrieval Trigger.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Ali šira implikacija je važnija. Pouzdan AI ne zahteva samo pristup znanju. Potreban mu je metod za određivanje kada je njegovo trenutno znanje nedovoljno.\u003C\u002Fp>\n\u003Cpre class=\"code-block\">\u003Ccode>Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Retrieval Trigger određuje kada sistem treba da traži dokaze. Answer Validity Boundary određuje da li su ti dokazi dovoljni.\u003C\u002Fp>\n\u003Cp>Zajedno opisuju nešto korisnije od samog RAG-a: proces odlučivanja za prelazak od onoga što AI izgleda da zna ka onome što zaista može da podrži.\u003C\u002Fp>\n\u003Ch2 id=\"section-157\">Primarni izvori\u003C\u002Fh2>\n\u003Cp>Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Temeljni RAG rad koji opisuje kombinaciju parametarske memorije modela sa spoljašnjom neparametarskom memorijom.\u003C\u002Fp>\n\u003Cp>Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Uvodi FLARE i aktivnu pretragu tokom generisanja, uključujući pretragu zasnovanu na predviđenom sadržaju niske pouzdanosti.\u003C\u002Fp>\n\u003Cp>Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Istražuje adaptivnu pretragu na zahtev i samorefleksiju umesto bezuslovne fiksne pretrage.\u003C\u002Fp>\n\u003Cp>Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dinamički bira između bez pretrage, jednostepene pretrage i složenijih strategija pretrage u skladu sa pristiglim pitanjem.\u003C\u002Fp>",{"time":212,"blocks":213,"version":1044},1790575114965,[214,220,226,231,236,241,246,251,259,266,271,276,281,286,291,297,302,307,312,317,322,327,348,353,358,363,368,373,378,383,388,393,398,403,408,413,418,423,428,433,438,443,448,453,458,463,468,473,478,483,488,493,498,503,508,513,518,523,528,533,538,543,548,553,558,563,568,573,578,583,588,593,598,603,608,613,618,623,628,633,638,643,648,653,658,663,668,673,678,683,688,693,698,703,708,714,719,724,729,734,739,744,749,754,759,764,769,774,779,784,789,794,799,804,809,814,819,824,829,834,839,844,849,854,859,864,869,874,879,884,889,894,899,904,909,914,919,924,929,934,939,944,949,954,959,964,969,974,979,984,989,994,999,1004,1009,1014,1019,1024,1029,1034,1039],{"id":215,"data":216,"type":42,"tunes":219},"Wt7UfNeFlS",{"text":217,"level":218},"Pitanje",2,{},{"id":221,"data":222,"type":224,"tunes":225},"T-ZCQblBzm",{"text":223},"Kada AI treba da prestane da se oslanja na ono što već zna i da pre odgovaranja pribavi spoljne informacije?","paragraph",{},{"id":227,"data":228,"type":224,"tunes":230},"vBcd4061WS",{"text":229},"Ovo pitanje izgleda jednostavno, ali se nalazi u središtu jedne od najvažnijih dizajnerskih odluka u savremenim AI sistemima.",{},{"id":232,"data":233,"type":224,"tunes":235},"r9NZ-Fzw0e",{"text":234},"Veliki jezički modeli sadrže značajno znanje u svojim parametrima. Generisanje uz pomoć pretrage dodaje spoljne informacije u vreme izvršavanja. Ali nijedna krajnost nije idealna.",{},{"id":237,"data":238,"type":224,"tunes":240},"CpzlgJjAVL",{"text":239},"Stalno verovanje modelu može da proizvede zastarele ili nepotkrepljene odgovore. Stalno pribavljanje informacija dodaje latenciju, troškove, irelevantan kontekst i nove mogućnosti za greške u pretrazi.",{},{"id":242,"data":243,"type":224,"tunes":245},"yeclJhYJ1a",{"text":244},"Pravi problem stoga nastupa pre RAG-a: Kada uopšte treba da se izvrši pretraga?",{},{"id":247,"data":248,"type":224,"tunes":250},"FgLSWvpZMg",{"text":249},"Ovaj članak koristi termin okidač za pretragu za tu odluku. Okidač za pretragu ovde nije predstavljen kao standardizovan termin iz istraživačke literature. To je praktičan sistemski koncept koji objedinjuje ideje već vidljive u istraživanjima o aktivnoj, adaptivnoj i samorefleksivnoj pretrazi.",{},{"id":252,"data":253,"type":257,"tunes":258},"Muzvv-2uzU",{"text":254,"caption":255,"alignment":256},"Okidač za pretragu je uslov koji ukazuje da AI sistem treba da prestane da se oslanja isključivo na znanje internog modela i da pribavi spoljne dokaze pre nego što proizvede ili finalizuje odgovor.","Radna definicija","left","quote",{},{"id":260,"data":261,"type":264,"tunes":265},"1BGt1waZ01",{"title":262,"maxLevel":263,"minLevel":218},"Sadržaj",3,"tableOfContents",{},{"id":267,"data":268,"type":42,"tunes":270},"BFKJ2htjYN",{"text":269,"level":218},"Šta to zapravo znači",{},{"id":272,"data":273,"type":224,"tunes":275},"yfBYqVwObv",{"text":274},"LLM ima dva suštinski različita načina dobijanja informacija.",{},{"id":277,"data":278,"type":224,"tunes":280},"Y4JYebztDi",{"text":279},"Prvi je znanje modela. To su informacije predstavljene u naučenim parametrima modela. Nije potreban upit ka bazi podataka, veb pretraga niti pretraživanje dokumenata u vreme izvršavanja.",{},{"id":282,"data":283,"type":224,"tunes":285},"x2L37FSTBK",{"text":284},"Drugi je znanje u vreme izvršavanja. To su informacije koje se pružaju dok model radi: rezultati pretrage, zapisi iz baze podataka, dokumenti, API-ji, korisnički fajlovi, izlazi alata ili drugi pribavljeni dokazi.",{},{"id":287,"data":288,"type":224,"tunes":290},"2szDUb7_-4",{"text":289},"RAG povezuje ova dva sveta. Ali sam RAG ne odgovara na pitanje kada ta veza treba da se aktivira. Za to služi okidač za pretragu.",{},{"id":292,"data":293,"type":295,"tunes":296},"5_yjTthHV4",{"code":294},"Question\n   ↓\nModel Knowledge\n   ↓\nIs internal knowledge sufficient?\n   ↓\nRetrieval Trigger\n   ↓\nExternal Retrieval, if required\n   ↓\nEvidence\n   ↓\nReasoning\n   ↓\nAnswer Validity Boundary\n   ↓\nAnswer","code",{},{"id":298,"data":299,"type":224,"tunes":301},"rH2K36ambR",{"text":300},"Okidač za pretragu stoga nastupa pre pretrage. Granica valjanosti odgovora nastupa kasnije.",{},{"id":303,"data":304,"type":224,"tunes":306},"L0WlGs_dTF",{"text":305},"Prvi pita: Da li su mi potrebni spoljni dokazi?",{},{"id":308,"data":309,"type":224,"tunes":311},"JyE4O9aDCW",{"text":310},"Drugi pita: Da li sada imam dovoljno dokaza da potkrepim ovaj odgovor?",{},{"id":313,"data":314,"type":224,"tunes":316},"L9JP5xByy4",{"text":315},"Ovo su povezane odluke, ali nisu ista odluka.",{},{"id":318,"data":319,"type":42,"tunes":321},"4hPbiDSHek",{"text":320,"level":218},"Najjednostavniji primer",{},{"id":323,"data":324,"type":224,"tunes":326},"cER32Me6gA",{"text":325},"Razmotrite tri pitanja.",{},{"id":328,"data":329,"type":346,"tunes":347},"izi7nU9FE9",{"content":330,"stretched":43,"withHeadings":14},[331,334,338,342],[217,332,333],"Interno znanje","Okidač za pretragu",[335,336,337],"Koji je glavni grad Francuske?","Obično dovoljno","Nema jakog okidača",[339,340,341],"Koja je trenutna cena akcija NVIDIA?","Potencijalno zastarelo","Pokreni pretragu",[343,344,345],"Da li ovaj novi naučni rad dokazuje da X izaziva Y?","Ne može se utvrditi tvrdnja bez ispitivanja dokaza","Jak okidač za pretragu","table",{},{"id":349,"data":350,"type":224,"tunes":352},"cb-Kx0fKs4",{"text":351},"Prvo pitanje se zasniva na veoma stabilnoj činjenici.",{},{"id":354,"data":355,"type":295,"tunes":357},"MZJzwvZUH7",{"code":356},"User\n↓\n\"What is the capital of France?\"\n\nModel knowledge\n↓\nParis\n\nFresh external evidence required?\n↓\nNo\n\nAnswer\n↓\nParis",{},{"id":359,"data":360,"type":224,"tunes":362},"fRP7-aWTJB",{"text":361},"Preuzimanje dokumenata pre odgovaranja obično bi dodalo malo vrednosti.",{},{"id":364,"data":365,"type":224,"tunes":367},"O2TaSvLoxO",{"text":366},"Sada razmotrite pitanje čiji se odgovor neprekidno menja.",{},{"id":369,"data":370,"type":295,"tunes":372},"cNv0Dp7Mk3",{"code":371},"User\n↓\n\"What is the current NVIDIA stock price?\"\n\nModel knowledge\n↓\nPotentially outdated\n\nCurrent information required?\n↓\nYes\n\nRETRIEVAL TRIGGER\n↓\nMarket data \u002F search \u002F API\n↓\nAnswer",{},{"id":374,"data":375,"type":224,"tunes":377},"Y3NDw8awnA",{"text":376},"Model može znati mnogo o NVIDIA. To ne znači da zna cenu sada.",{},{"id":379,"data":380,"type":224,"tunes":382},"FwjiaA6mdJ",{"text":381},"Treći primer je još važniji.",{},{"id":384,"data":385,"type":295,"tunes":387},"G48ZGtX4XK",{"code":386},"User\n↓\n\"Does this new scientific paper prove that X causes Y?\"\n\nModel knowledge\n↓\nCan reason about causality,\nstatistics and scientific methodology.\n\nBut:\nthe actual evidence is not available internally.\n\nRETRIEVAL TRIGGER\n↓\nRetrieve the paper\n↓\nInspect methodology\n↓\nInspect results\n↓\nCompare claim with evidence\n↓\nAnswer Validity Boundary\n↓\nAnswer",{},{"id":389,"data":390,"type":224,"tunes":392},"nGu-KcQC6l",{"text":391},"Sposobnost zaključivanja modela može biti savršeno korisna. Nedostaje komponenta dokaza.",{},{"id":394,"data":395,"type":224,"tunes":397},"_bUxnOYvHG",{"text":396},"Ta razlika je fundamentalna.",{},{"id":399,"data":400,"type":42,"tunes":402},"etbE_esRx4",{"text":401,"level":218},"Gde primer prestaje da funkcioniše",{},{"id":404,"data":405,"type":224,"tunes":407},"0iSdy2Msw7",{"text":406},"Gornji primeri čine da odluka izgleda binarno: preuzmi ili ne preuzimaj.",{},{"id":409,"data":410,"type":224,"tunes":412},"8Go7nm2niJ",{"text":411},"Stvarni sistemi su složeniji. Pitanje može sadržati nekoliko tvrdnji, neke stabilne, a neke aktuelne. Preuzeti dokumenti mogu se ne slagati. Pretraživač može vratiti irelevantne informacije. Relevantne informacije mogu postojati, ali ne uspeti da se rangiraju dovoljno visoko. Dokument može biti autoritativan, ali zastareo.",{},{"id":414,"data":415,"type":224,"tunes":417},"8dVjRU5cXg",{"text":416},"Sama pretraga takođe može uneti netačan kontekst u inače razuman odgovor.",{},{"id":419,"data":420,"type":224,"tunes":422},"pLqSH5-OJR",{"text":421},"Zbog toga pretragu ne treba tretirati kao automatski sinonim za istinu.",{},{"id":424,"data":425,"type":224,"tunes":427},"ww4Od2cmTr",{"text":426},"Istraživanje o adaptivnoj pretrazi sve se više udaljava od pretpostavke da svaki upit treba da dobije istu strategiju pretrage.",{},{"id":429,"data":430,"type":224,"tunes":432},"1yE2LUP7cF",{"text":431},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, na primer, eksplicitno istražuje pretragu na zahtev, umesto da neselektivno preuzima fiksni broj odlomaka za svaki unos. Autori raspravljaju o tome kako nepotrebna ili irelevantna pretraga može smanjiti kvalitet odgovora.",{},{"id":434,"data":435,"type":224,"tunes":437},"915QBDW89m",{"text":436},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> na sličan način bira između pretrage bez pretrage, jednostepene pretrage i složenijih strategija pretrage u zavisnosti od složenosti pitanja.",{},{"id":439,"data":440,"type":224,"tunes":442},"1FBgxY0QQp",{"text":441},"Dakle, važno pitanje nije: Da li ovaj sistem ima RAG?",{},{"id":444,"data":445,"type":224,"tunes":447},"4xdj86u8Qz",{"text":446},"Već: Može li ovaj sistem da prepozna kada je pretraga neophodna i koja vrsta pretrage je odgovarajuća?",{},{"id":449,"data":450,"type":42,"tunes":452},"bGPa0AsJI6",{"text":451,"level":218},"Direktan odgovor",{},{"id":454,"data":455,"type":224,"tunes":457},"fBKcyJ0IcX",{"text":456},"AI treba da pokrene pretragu kada odgovaranje zahteva informacije koje njegovo interno znanje modela ne može bezbedno da pruži sa potrebnom svežinom, specifičnošću, poreklom ili dokaznom podrškom.",{},{"id":459,"data":460,"type":224,"tunes":462},"JbIPIJjkXK",{"text":461},"U praktičnim sistemima, okidač za pretragu može proizaći iz nekoliko uslova:",{},{"id":464,"data":465,"type":295,"tunes":467},"_JbTSHlrtH",{"code":466},"Need for current information\n        OR\nNeed for exact source-specific information\n        OR\nNeed for evidence or provenance\n        OR\nNeed for private\u002Fuser-specific information\n        OR\nInsufficient knowledge coverage\n        OR\nConflicting evidence\n        OR\nHigh consequence of factual error",{},{"id":469,"data":470,"type":224,"tunes":472},"Aaem6fQ_tF",{"text":471},"Ako nijedan od ovih uslova nije materijalno prisutan, pretraga može biti nepotrebna. Ako je jedan ili više njih prisutno, spoljni dokazi postaju deo procesa generisanja odgovora.",{},{"id":474,"data":475,"type":42,"tunes":477},"x2DDg7Ue1-",{"text":476,"level":218},"Zašto je to tako",{},{"id":479,"data":480,"type":224,"tunes":482},"1GTaWG9ViB",{"text":481},"Interno znanje jezičkog modela često se opisuje kao parametarsko znanje. Ono je naučeno tokom obuke i kodirano u parametre modela.",{},{"id":484,"data":485,"type":224,"tunes":487},"klwNY3lr1d",{"text":486},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Originalni RAG rad Lewisa i saradnika\u003C\u002Fa> predstavio je pretragu kao kombinaciju ove parametarske memorije sa spoljnom, neparametarskom memorijom. Spoljna memorija može se pretraživati i ažurirati bez ponovnog obučavanja celokupnog jezičkog modela.",{},{"id":489,"data":490,"type":224,"tunes":492},"Fyw2AVDbxR",{"text":491},"Ova razlika stvara neizbežan sistemski problem.",{},{"id":494,"data":495,"type":224,"tunes":497},"4FvbthV3in",{"text":496},"Model može da zna stvari. Ali model ne može pretpostaviti da je sve što zna aktuelno, potpuno, dovoljno specifično i potkrepljeno potrebnim dokazima.",{},{"id":499,"data":500,"type":224,"tunes":502},"asxdihTbcB",{"text":501},"Model stoga može proizvesti lingvistički uverljiv odgovor, a da pritom posluje izvan tačke u kojoj je njegovo interno znanje dovoljno.",{},{"id":504,"data":505,"type":224,"tunes":507},"jGgq116uAa",{"text":506},"Ta tačka je mesto gde okidač za pretragu postaje koristan.",{},{"id":509,"data":510,"type":42,"tunes":512},"T6q_BUeDg3",{"text":511,"level":218},"Kontekst",{},{"id":514,"data":515,"type":224,"tunes":517},"9H_bNlyoYs",{"text":516},"Tradicionalni RAG često izgleda ovako:",{},{"id":519,"data":520,"type":295,"tunes":522},"YSZR1AInSj",{"code":521},"Question\n↓\nRetrieve documents\n↓\nAdd documents to context\n↓\nGenerate answer",{},{"id":524,"data":525,"type":224,"tunes":527},"HnzY2Q9xTs",{"text":526},"Ova arhitektura pretpostavlja pretragu pre generisanja. To dobro funkcioniše za mnoge aplikacije intenzivne znanjem, ali može izvršiti i nepotrebnu pretragu.",{},{"id":529,"data":530,"type":224,"tunes":532},"Aho03YTGAU",{"text":531},"Napredniji pristupi uvode adaptivni korak:",{},{"id":534,"data":535,"type":295,"tunes":537},"uK0l0tYLg6",{"code":536},"Question\n↓\nEvaluate information requirement\n↓\n        ┌───────────────┐\n        │               │\n   no retrieval      retrieval\n        │               │\n        ↓               ↓\n model knowledge    external evidence\n        │               │\n        └───────┬───────┘\n                ↓\n              answer",{},{"id":539,"data":540,"type":224,"tunes":542},"Upb-15aN8T",{"text":541},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> ide dalje razmatrajući pretragu tokom samog generisanja. Koristi predstojeće generisanje i tokene niske pouzdanosti kao signale za preuzimanje dodatnih informacija.",{},{"id":544,"data":545,"type":224,"tunes":547},"I5hs5j9IKc",{"text":546},"Self-RAG na sličan način uvodi mehanizme koji omogućavaju pretrazi, generisanju i kritici da interaguju umesto da se pretraga tretira kao bezuslovni korak predobrade.",{},{"id":549,"data":550,"type":224,"tunes":552},"mw2jbuWA-g",{"text":551},"Adaptive-RAG pristupa istom širem problemu iz ugla složenosti upita: različita pitanja mogu zahtevati različite strategije pretrage.",{},{"id":554,"data":555,"type":224,"tunes":557},"DUba0EfbWg",{"text":556},"Ovi pristupi se tehnički razlikuju. Ali otkrivaju isti arhitektonski uvid: Pretraga bi trebalo da bude odluka, a ne samo trajni prekidač.",{},{"id":559,"data":560,"type":42,"tunes":562},"wzX0jC8H8b",{"text":561,"level":218},"Pretpostavke",{},{"id":564,"data":565,"type":224,"tunes":567},"4puAk8h-NF",{"text":566},"Okvir Retrieval Trigger pretpostavlja da sistem ima pristup najmanje jednom eksternom izvoru informacija kada je pretraga potrebna.",{},{"id":569,"data":570,"type":224,"tunes":572},"Qsm42lc7aC",{"text":571},"Taj izvor može biti web pretraga, skladište dokumenata, vektorska baza podataka, SQL baza podataka, graf znanja, API, poslovni sistem, dokument koji je otpremio korisnik ili izlaz alata.",{},{"id":574,"data":575,"type":224,"tunes":577},"Rznt7yvqT2",{"text":576},"Takođe pretpostavlja da pretraga ima cenu. Ta cena ne mora biti finansijska.",{},{"id":579,"data":580,"type":224,"tunes":582},"wQoEfZuFPe",{"text":581},"Pretraga uvodi latenciju, potrošnju tokena, korišćenje konteksta, složenost infrastrukture i mogućnost preuzimanja obmanjujućih informacija.",{},{"id":584,"data":585,"type":224,"tunes":587},"WM1F9QkT2G",{"text":586},"Optimalni sistem stoga ne maksimizuje pretragu. On maksimizuje odgovarajuću pretragu.",{},{"id":589,"data":590,"type":42,"tunes":592},"Z4gw9SX7jo",{"text":591,"level":218},"Promenljive",{},{"id":594,"data":595,"type":224,"tunes":597},"Z_sKNO6vmp",{"text":596},"Praktični Retrieval Trigger može razmotriti pet primarnih promenljivih.",{},{"id":599,"data":600,"type":42,"tunes":602},"Eti88tz1T6",{"text":601,"level":263},"Svežina",{},{"id":604,"data":605,"type":224,"tunes":607},"3zKe198lls",{"text":606},"Kolika je verovatnoća da su tražene informacije promenjene? Glavni grad Francuske ima veoma nisku volatilnost. Cena akcija ima izuzetno visoku volatilnost.",{},{"id":609,"data":610,"type":42,"tunes":612},"ryQRR7TzC7",{"text":611,"level":263},"Specifičnost",{},{"id":614,"data":615,"type":224,"tunes":617},"bkXBBuCBb_",{"text":616},"Da li pitanje zahteva informacije iz određenog izvora, dokumenta, organizacije, naloga ili skupa podataka? Ako korisnik pita šta piše u konkretnom ugovoru, opšte znanje modela je irelevantno. Ugovor mora biti pronađen.",{},{"id":619,"data":620,"type":42,"tunes":622},"LlT6c-tPU2",{"text":621,"level":263},"Zahtev za dokazima",{},{"id":624,"data":625,"type":224,"tunes":627},"1G-aWjGT1c",{"text":626},"Da li odgovor zahteva poreklo? Model može znati da je tvrdnja opšteprihvaćena, ali i dalje može biti potreban izvor kada zadatak zahteva verifikaciju.",{},{"id":629,"data":630,"type":42,"tunes":632},"lnoOCm4KDw",{"text":631,"level":263},"Pokrivenost znanja",{},{"id":634,"data":635,"type":224,"tunes":637},"nKGrZO0Zw0",{"text":636},"Da li je verovatno da je tema adekvatno zastupljena u internom znanju modela? Retke, vlasničke, visoko lokalne ili novoobjavljene informacije stvaraju veći pritisak za pretragu.",{},{"id":639,"data":640,"type":42,"tunes":642},"SYp_4G0qXz",{"text":641,"level":263},"Posledica greške",{},{"id":644,"data":645,"type":224,"tunes":647},"YJeo8nKsl9",{"text":646},"Nema svaki netačan odgovor isti uticaj. Kada faktografska tačnost materijalno utiče na odluku, prihvatljiv prag dokaza može biti viši.",{},{"id":649,"data":650,"type":224,"tunes":652},"NTh27HJjo1",{"text":651},"Ove promenljive ne moraju biti implementirane kao doslovne numeričke ocene. One opisuju površinu odlučivanja.",{},{"id":654,"data":655,"type":42,"tunes":657},"A25id0cm1s",{"text":656,"level":218},"Dijagnostička \u002F metoda odlučivanja",{},{"id":659,"data":660,"type":224,"tunes":662},"az70f7cIIF",{"text":661},"Vrlo jednostavan okidač za pretragu može se implementirati bez mašinskog učenja.",{},{"id":664,"data":665,"type":295,"tunes":667},"yUFgVx9VRM",{"code":666},"def should_retrieve(\n    time_sensitive=False,\n    source_specific=False,\n    evidence_required=False,\n    private_context=False,\n    knowledge_uncertain=False,\n    conflicting_information=False\n):\n    return any([\n        time_sensitive,\n        source_specific,\n        evidence_required,\n        private_context,\n        knowledge_uncertain,\n        conflicting_information,\n    ])",{},{"id":669,"data":670,"type":224,"tunes":672},"tBn6sOGnKB",{"text":671},"Za stabilno faktografsko pitanje:",{},{"id":674,"data":675,"type":295,"tunes":677},"BW2rsTbqqL",{"code":676},"should_retrieve()\n# False",{},{"id":679,"data":680,"type":224,"tunes":682},"iriE0iq97f",{"text":681},"Za trenutnu cenu akcija:",{},{"id":684,"data":685,"type":295,"tunes":687},"d10aolm-TW",{"code":686},"should_retrieve(\n    time_sensitive=True\n)\n# True",{},{"id":689,"data":690,"type":224,"tunes":692},"nwL_vpUi-Y",{"text":691},"Za naučnu tvrdnju:",{},{"id":694,"data":695,"type":295,"tunes":697},"-DXgs4BBKH",{"code":696},"should_retrieve(\n    source_specific=True,\n    evidence_required=True\n)\n# True",{},{"id":699,"data":700,"type":224,"tunes":702},"Wj1sAbZ7l8",{"text":701},"Proizvodni sistemi mogu ovu odluku učiniti daleko sofisticiranijom. Klasifikator bi mogao da predvidi zahteve za pretragu. Model bi mogao da emituje specijalne kontrolne tokene. Ruter bi mogao da klasifikuje složenost upita. Pretraga bi takođe mogla biti pokrenuta više puta tokom generisanja.",{},{"id":704,"data":705,"type":224,"tunes":707},"loLbe4TkAK",{"text":706},"Implementacija se može promeniti. Arhitektonsko pitanje ostaje isto:",{},{"id":709,"data":710,"type":257,"tunes":713},"T2PJWaSYp9",{"text":711,"caption":712,"alignment":256},"Da li su dokazi trenutno dostupni modelu dovoljni za odgovor koji će proizvesti?","",{},{"id":715,"data":716,"type":42,"tunes":718},"9Alw1zzG4E",{"text":717,"level":218},"Dokazi",{},{"id":720,"data":721,"type":224,"tunes":723},"OgqdUwG1J-",{"text":722},"Koncept predložen ovde je u skladu sa nekoliko pravaca istraživanja pretrage.",{},{"id":725,"data":726,"type":224,"tunes":728},"QxIkEDnlBu",{"text":727},"Originalna \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">RAG arhitektura\u003C\u002Fa> pokazala je korisnost kombinovanja parametarskog znanja modela sa eksternim neparametarskim znanjem, posebno za zadatke intenzivne znanjem.",{},{"id":730,"data":731,"type":224,"tunes":733},"nqdi_kDLE3",{"text":732},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> eksplicitno istražuje aktivnu pretragu tokom generisanja, uključujući pretragu podstaknutu sadržajem koji sledi sa niskom pouzdanošću.",{},{"id":735,"data":736,"type":224,"tunes":738},"UThAFgyEe3",{"text":737},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demonstrira arhitekturu u kojoj se pretraga može obaviti na zahtev i nakon koje sledi refleksija o pronađenim odlomcima i generisanom sadržaju.",{},{"id":740,"data":741,"type":224,"tunes":743},"CT8n5F5KLR",{"text":742},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> dinamički bira između različitih strategija u zavisnosti od složenosti pitanja, uključujući situacije kada pretraga nije potrebna.",{},{"id":745,"data":746,"type":224,"tunes":748},"RDjo1UGf2s",{"text":747},"Termin Retrieval Trigger se ovde koristi kao apstrakcija na nivou sistema nad ovom širom familijom odluka.",{},{"id":750,"data":751,"type":224,"tunes":753},"nmWZ-exi8b",{"text":752},"Ne tvrdi se da ovi radovi koriste istu terminologiju. Umesto toga, identifikuje se zajednički arhitektonski problem: Šta uzrokuje da AI sistem pređe sa internog znanja na eksterne dokaze?",{},{"id":755,"data":756,"type":42,"tunes":758},"8a-H_OlfG1",{"text":757,"level":218},"Stvarni primeri",{},{"id":760,"data":761,"type":224,"tunes":763},"l0KONt5Buo",{"text":762},"Razmotrite asistenta za podršku povezanog sa dokumentacijom kompanije.",{},{"id":765,"data":766,"type":295,"tunes":768},"MOy11BOFq5",{"code":767},"\"How do I reset my password?\"",{},{"id":770,"data":771,"type":224,"tunes":773},"tsZcuMS1sT",{"text":772},"Ako je procedura stabilna i pouzdano predstavljena u trenutnim instrukcijama asistenta, direktno odgovaranje može biti prikladno.",{},{"id":775,"data":776,"type":295,"tunes":778},"l53NPB6WNV",{"code":777},"\"What permissions does my account currently have?\"",{},{"id":780,"data":781,"type":224,"tunes":783},"RKQXMbXA29",{"text":782},"Ta informacija je specifična za korisnika i dinamična. Okidač za preuzimanje se aktivira. Sistem mora da pregleda stvarne podatke o nalogu ili autorizaciji.",{},{"id":785,"data":786,"type":295,"tunes":788},"b5tNqxBKir",{"code":787},"\"Why was my production deployment rejected yesterday?\"",{},{"id":790,"data":791,"type":224,"tunes":793},"xaP7a7lV3i",{"text":792},"Model može da razume sisteme za raspoređivanje i objasni uobičajene razloge. Ali pitanje se odnosi na konkretan događaj. Potrebni su dnevnici, CI\u002FCD izlaz ili zapisi o incidentima.",{},{"id":795,"data":796,"type":224,"tunes":798},"SlBdofaCVq",{"text":797},"Ista logika važi i za web pretragu.",{},{"id":800,"data":801,"type":295,"tunes":803},"VgFaQjUMnU",{"code":802},"\"What is RAG?\"",{},{"id":805,"data":806,"type":224,"tunes":808},"6BG7aSJQzt",{"text":807},"Opšte objašnjenje možda ne zahteva preuzimanje.",{},{"id":810,"data":811,"type":295,"tunes":813},"TRngQB41uY",{"code":812},"\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\"",{},{"id":815,"data":816,"type":224,"tunes":818},"7UIuDjIRyG",{"text":817},"Sada su potrebni dokazi specifični za izvor.",{},{"id":820,"data":821,"type":295,"tunes":823},"CG2PbVS1yz",{"code":822},"\"What is the latest research on adaptive retrieval?\"",{},{"id":825,"data":826,"type":224,"tunes":828},"G1gyMnE_E8",{"text":827},"Ovo uvodi i zahtev za svežinom. Osnovna tema se nije promenila. Zahtev za informacijom jeste.",{},{"id":830,"data":831,"type":42,"tunes":833},"NyJtHsPsSf",{"text":832,"level":218},"Uobičajene zablude i načini neuspeha",{},{"id":835,"data":836,"type":224,"tunes":838},"1otM6VenxR",{"text":837},"Više preuzimanja automatski proizvodi bolji odgovor. Ne. Irelevantni dokumenti troše kontekst i mogu da odvrate generisanje.",{},{"id":840,"data":841,"type":224,"tunes":843},"7BpMfX7lOZ",{"text":842},"Visoka pouzdanost modela znači da preuzimanje nije potrebno. Model može samouvereno da proizvede netačan odgovor. Samoprijavljena pouzdanost stoga ne treba da se tretira kao jedini okidač.",{},{"id":845,"data":846,"type":224,"tunes":848},"THz75XkfrR",{"text":847},"Uspešno preuzimanje znači da je odgovor verifikovan. Preuzimanje samo pruža kandidatske dokaze. Dokazi i dalje moraju biti relevantni, dovoljno autoritativni i ispravno interpretirani.",{},{"id":850,"data":851,"type":224,"tunes":853},"gOUGv2dAaq",{"text":852},"RAG automatski rešava zastarelo znanje. To čini samo ako sam korpus za preuzimanje sadrži ažurne informacije. Preuzimanje zastarelog dokumenta ne stvara ažuran odgovor.",{},{"id":855,"data":856,"type":224,"tunes":858},"Mz8i-je--k",{"text":857},"Jedan korak preuzimanja je uvek dovoljan. Složena pitanja mogu zahtevati nekoliko dokaza ili iterativno preuzimanje.",{},{"id":860,"data":861,"type":42,"tunes":863},"imAEotM35y",{"text":862,"level":218},"Rubni slučajevi",{},{"id":865,"data":866,"type":224,"tunes":868},"8xkcG8hc9c",{"text":867},"Neka pitanja sadrže i stabilne i nestabilne informacije.",{},{"id":870,"data":871,"type":295,"tunes":873},"IYDiRezoWn",{"code":872},"\"Who founded NVIDIA, and what is its market capitalization today?\"",{},{"id":875,"data":876,"type":224,"tunes":878},"2kxOM8vxYh",{"text":877},"Prvi deo možda može da se odgovori na osnovu stabilnog znanja modela. Drugi deo zahteva aktuelne informacije.",{},{"id":880,"data":881,"type":224,"tunes":883},"6PyzlxURFS",{"text":882},"Dovoljno sposoban sistem ne bi trebalo nužno da tretira ceo upit kao jednu odluku o pretraživanju. Može da pokrene pretraživanje samo tamo gde je potrebno.",{},{"id":885,"data":886,"type":224,"tunes":888},"lsbZ8aQAD6",{"text":887},"Još jedan granični slučaj je neslaganje između izvora. Pretpostavimo da pretraživanje vrati tri dokumenta koji daju nekompatibilne tvrdnje.",{},{"id":890,"data":891,"type":224,"tunes":893},"-Y67JvJusX",{"text":892},"Okidač za pretraživanje je već uspeo: sistem je prepoznao da su spoljni dokazi bili potrebni. Ali zadatak nije završen.",{},{"id":895,"data":896,"type":224,"tunes":898},"32VdDErqUM",{"text":897},"Sistem je sada došao do problema procene dokaza. Tu granica valjanosti odgovora postaje važna.",{},{"id":900,"data":901,"type":224,"tunes":903},"edCyD-PqlU",{"text":902},"Sistem je možda preuzeo informacije i još uvek ne poseduje dovoljno dokaza da donese čvrst zaključak.",{},{"id":905,"data":906,"type":295,"tunes":908},"rmjW0MBcFo",{"code":907},"Retrieval Trigger\n≠\npermission to answer",{},{"id":910,"data":911,"type":224,"tunes":913},"gZBq0voX0-",{"text":912},"Okidač pribavlja dokaze. Granica valjanosti određuje da li su ti dokazi dovoljni.",{},{"id":915,"data":916,"type":42,"tunes":918},"DmO9cFY93l",{"text":917,"level":218},"Ograničenja",{},{"id":920,"data":921,"type":224,"tunes":923},"gV4YT_2O1X",{"text":922},"Okidač za pretraživanje je konceptualni okvir, a ne univerzalni algoritam.",{},{"id":925,"data":926,"type":224,"tunes":928},"7c6OA2X4-H",{"text":927},"Različiti sistemi će zahtevati različita pravila okidanja. Bot za korisničku podršku, asistent za naučna istraživanja, pretraživač i autonomni softverski agent nemaju identične zahteve za dokazima.",{},{"id":930,"data":931,"type":224,"tunes":933},"Xn8K4ArjdA",{"text":932},"Pragovi okidanja takođe mogu da stvore sopstvene načine neuspeha. Prag koji je prenizak izaziva prekomerno pretraživanje. Prag koji je previsok izaziva nepotkrepljeno odgovaranje.",{},{"id":935,"data":936,"type":224,"tunes":938},"y0gYRFZx6m",{"text":937},"Sama infrastruktura za pretraživanje je takođe važna. Savršen okidač povezan sa lošom kolekcijom izvora i dalje proizvodi loše dokaze.",{},{"id":940,"data":941,"type":224,"tunes":943},"Kcvx1v4Z1x",{"text":942},"Slično tome, odlična baza znanja pruža malu vrednost ako se okidač nikada ne aktivira kada je potreban.",{},{"id":945,"data":946,"type":224,"tunes":948},"wcRpKvBhkb",{"text":947},"Okidač za pretraživanje stoga rešava samo jedan deo veće arhitekture.",{},{"id":950,"data":951,"type":42,"tunes":953},"YN1_g7vs7V",{"text":952,"level":218},"Šta bi promenilo ovaj odgovor?",{},{"id":955,"data":956,"type":224,"tunes":958},"4PYX_PYK_Z",{"text":957},"Budući modeli mogu da sadrže bolje mehanizme za identifikovanje sopstvenih ograničenja znanja. Pretraživači mogu postati jeftiniji i brži. Sistemi sa dugim kontekstom mogu neprekidno da nose mnogo više izvornog materijala.",{},{"id":960,"data":961,"type":224,"tunes":963},"vyqJ8Kp8Ye",{"text":962},"Modeli takođe mogu sve više kombinovati pretragu, baze podataka, alate i strukturisano znanje bez izlaganja posebne RAG faze programeru aplikacije.",{},{"id":965,"data":966,"type":224,"tunes":968},"_KN0MPs6as",{"text":967},"Ove promene mogu izmeniti način na koji se trigger implementira. One ne uklanjaju nužno osnovnu odluku.",{},{"id":970,"data":971,"type":224,"tunes":973},"Zrlr4a0utJ",{"text":972},"Sve dok postoji razlika između informacija koje su modelu već dostupne i informacija koje se moraju pribaviti spolja, sistemu je i dalje potreban neki mehanizam za određivanje kada da pređe tu granicu.",{},{"id":975,"data":976,"type":224,"tunes":978},"4hl7r5cF1M",{"text":977},"Implementacija može nestati iz vida. Arhitektonsko pitanje ostaje.",{},{"id":980,"data":981,"type":42,"tunes":983},"o_g5g_6eSj",{"text":982,"level":218},"Zaključak",{},{"id":985,"data":986,"type":224,"tunes":988},"L6MWm8xdAg",{"text":987},"RAG počinje previše kasno da bi objasnio ceo problem.",{},{"id":990,"data":991,"type":224,"tunes":993},"uymgoYlFM5",{"text":992},"Pre nego što pretraga može da se dogodi, AI sistem mora da utvrdi da li je pretraga neophodna. Ta odluka je Retrieval Trigger.",{},{"id":995,"data":996,"type":295,"tunes":998},"_KIblg0ae_",{"code":997},"Stable known fact\n→ answer from model knowledge\n\nCurrent fact\n→ retrieve\n\nSource-specific or evidence-dependent claim\n→ retrieve and verify",{},{"id":1000,"data":1001,"type":224,"tunes":1003},"unCgfbeYI5",{"text":1002},"Ali šira implikacija je važnija. Pouzdan AI ne zahteva samo pristup znanju. Potreban mu je metod za određivanje kada je njegovo trenutno znanje nedovoljno.",{},{"id":1005,"data":1006,"type":295,"tunes":1008},"ZAosU4trn9",{"code":1007},"Model Knowledge\n        ↓\nRetrieval Trigger\n        ↓\nRuntime Knowledge \u002F RAG\n        ↓\nEvidence\n        ↓\nReasoning\n        ↓\nAnswer Validity Boundary\n        ↓\nAnswer",{},{"id":1010,"data":1011,"type":224,"tunes":1013},"f0ZIysaJy1",{"text":1012},"Retrieval Trigger određuje kada sistem treba da traži dokaze. Answer Validity Boundary određuje da li su ti dokazi dovoljni.",{},{"id":1015,"data":1016,"type":224,"tunes":1018},"iCg9ojv75m",{"text":1017},"Zajedno opisuju nešto korisnije od samog RAG-a: proces odlučivanja za prelazak od onoga što AI izgleda da zna ka onome što zaista može da podrži.",{},{"id":1020,"data":1021,"type":42,"tunes":1023},"cDBiNnZJv-",{"text":1022,"level":218},"Primarni izvori",{},{"id":1025,"data":1026,"type":224,"tunes":1028},"8gumvODB16",{"text":1027},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Temeljni RAG rad koji opisuje kombinaciju parametarske memorije modela sa spoljašnjom neparametarskom memorijom.",{},{"id":1030,"data":1031,"type":224,"tunes":1033},"Chz6I7zmlv",{"text":1032},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Uvodi FLARE i aktivnu pretragu tokom generisanja, uključujući pretragu zasnovanu na predviđenom sadržaju niske pouzdanosti.",{},{"id":1035,"data":1036,"type":224,"tunes":1038},"MRdjivpsoW",{"text":1037},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Istražuje adaptivnu pretragu na zahtev i samorefleksiju umesto bezuslovne fiksne pretrage.",{},{"id":1040,"data":1041,"type":224,"tunes":1043},"lck29euXJP",{"text":1042},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dinamički bira između bez pretrage, jednostepene pretrage i složenijih strategija pretrage u skladu sa pristiglim pitanjem.",{},"2.31","AI model ne zahteva pretragu za svako pitanje. Važan problem je znati kada njegovo interno znanje više nije dovoljno. Okidač za pretragu je praktična granica odlučivanja koja određuje kada AI sistem treba da prestane da se oslanja isključivo na znanje modela i pribavi spoljne dokaze pre odgovaranja.","\u002Fuploads\u002F2026\u002F09\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg.webp","when-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger-1790574991244-f4rpyg","PUBLISHED","2026-09-28T01:49:00.000Z","2026-09-28T05:49:59.593Z","2026-09-28T06:02:54.212Z",{"en":1053,"de":1054,"sr":1055,"es":1056,"fr":1057,"it":1058,"ru":1059,"zh":1060},"\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fde\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fsr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fes\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Ffr\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fit\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fru\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger","\u002Fzh\u002Fblog\u002Fwhen-should-an-ai-stop-trusting-its-own-knowledge-the-retrieval-trigger",[1062,1065,1069,1073,1077,1081],{"id":101,"name":1063,"slug":1064},"Overview","overview-digital-platform",{"id":1066,"name":1067,"slug":1068},57,"Granice podataka","data-boundaries",{"id":1070,"name":1071,"slug":1072},51,"Anti-obrasci","anti-patterns",{"id":1074,"name":1075,"slug":1076},58,"Evaluacija i gate-ovi kvaliteta","evaluation",{"id":1078,"name":1079,"slug":1080},56,"Portfolio slučajeva upotrebe","use-case-portfolio",{"id":1082,"name":1083,"slug":1084},60,"Kontrole troška i latencije","cost-and-latency",{"id":1086,"login":1087,"email":1088,"displayName":1089},"20","rooth8233","aleksandar@stajic.de","Aleksandar Stajić",[1091,1739],{"lang":1092,"title":1093,"content":1094,"contentJson":1095,"excerpt":1738},"en","When Should an AI Stop Trusting Its Own Knowledge? — The Retrieval Trigger","{\"time\":1790574879391,\"blocks\":[{\"id\":\"Wt7UfNeFlS\",\"data\":{\"text\":\"Question\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"T-ZCQblBzm\",\"data\":{\"text\":\"When should an AI stop relying on what it already knows and retrieve external information before answering?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vBcd4061WS\",\"data\":{\"text\":\"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"r9NZ-Fzw0e\",\"data\":{\"text\":\"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CpzlgJjAVL\",\"data\":{\"text\":\"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yeclJhYJ1a\",\"data\":{\"text\":\"The real problem therefore comes before RAG: When should retrieval happen at all?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FgLSWvpZMg\",\"data\":{\"text\":\"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Muzvv-2uzU\",\"data\":{\"text\":\"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.\",\"caption\":\"Working definition\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"1BGt1waZ01\",\"data\":{\"title\":\"Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\",\"tunes\":{}},{\"id\":\"BFKJ2htjYN\",\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"yfBYqVwObv\",\"data\":{\"text\":\"An LLM has two fundamentally different ways of obtaining information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Y4JYebztDi\",\"data\":{\"text\":\"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2L37FSTBK\",\"data\":{\"text\":\"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"2szDUb7_-4\",\"data\":{\"text\":\"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"5_yjTthHV4\",\"data\":{\"code\":\"Question\\n   ↓\\nModel Knowledge\\n   ↓\\nIs internal knowledge sufficient?\\n   ↓\\nRetrieval Trigger\\n   ↓\\nExternal Retrieval, if required\\n   ↓\\nEvidence\\n   ↓\\nReasoning\\n   ↓\\nAnswer Validity Boundary\\n   ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"rH2K36ambR\",\"data\":{\"text\":\"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L0WlGs_dTF\",\"data\":{\"text\":\"The first asks: Do I need external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JyE4O9aDCW\",\"data\":{\"text\":\"The second asks: Do I now have enough evidence to support this answer?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"L9JP5xByy4\",\"data\":{\"text\":\"These are related decisions, but they are not the same decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hPbiDSHek\",\"data\":{\"text\":\"Simplest Example\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"cER32Me6gA\",\"data\":{\"text\":\"Consider three questions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"izi7nU9FE9\",\"data\":{\"content\":[[\"Question\",\"Internal knowledge\",\"Retrieval Trigger\"],[\"What is the capital of France?\",\"Usually sufficient\",\"No strong trigger\"],[\"What is the current NVIDIA stock price?\",\"Potentially outdated\",\"Trigger retrieval\"],[\"Does this new scientific paper prove that X causes Y?\",\"Cannot establish the claim without examining the evidence\",\"Strong retrieval trigger\"]],\"stretched\":false,\"withHeadings\":true},\"type\":\"table\",\"tunes\":{}},{\"id\":\"cb-Kx0fKs4\",\"data\":{\"text\":\"The first question is based on a highly stable fact.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MZJzwvZUH7\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the capital of France?\\\"\\n\\nModel knowledge\\n↓\\nParis\\n\\nFresh external evidence required?\\n↓\\nNo\\n\\nAnswer\\n↓\\nParis\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"fRP7-aWTJB\",\"data\":{\"text\":\"Retrieving documents before answering would usually add little value.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"O2TaSvLoxO\",\"data\":{\"text\":\"Now consider a question whose answer changes continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cNv0Dp7Mk3\",\"data\":{\"code\":\"User\\n↓\\n\\\"What is the current NVIDIA stock price?\\\"\\n\\nModel knowledge\\n↓\\nPotentially outdated\\n\\nCurrent information required?\\n↓\\nYes\\n\\nRETRIEVAL TRIGGER\\n↓\\nMarket data \u002F search \u002F API\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Y3NDw8awnA\",\"data\":{\"text\":\"The model may know a great deal about NVIDIA. That does not mean it knows the price now.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"FwjiaA6mdJ\",\"data\":{\"text\":\"The third example is even more important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"G48ZGtX4XK\",\"data\":{\"code\":\"User\\n↓\\n\\\"Does this new scientific paper prove that X causes Y?\\\"\\n\\nModel knowledge\\n↓\\nCan reason about causality,\\nstatistics and scientific methodology.\\n\\nBut:\\nthe actual evidence is not available internally.\\n\\nRETRIEVAL TRIGGER\\n↓\\nRetrieve the paper\\n↓\\nInspect methodology\\n↓\\nInspect results\\n↓\\nCompare claim with evidence\\n↓\\nAnswer Validity Boundary\\n↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nGu-KcQC6l\",\"data\":{\"text\":\"The model's reasoning capability may be perfectly useful. The missing component is evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_bUxnOYvHG\",\"data\":{\"text\":\"That distinction is fundamental.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"etbE_esRx4\",\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"0iSdy2Msw7\",\"data\":{\"text\":\"The examples above make the decision appear binary: retrieve or do not retrieve.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8Go7nm2niJ\",\"data\":{\"text\":\"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8dVjRU5cXg\",\"data\":{\"text\":\"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"pLqSH5-OJR\",\"data\":{\"text\":\"This is why retrieval should not be treated as an automatic synonym for truth.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ww4Od2cmTr\",\"data\":{\"text\":\"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1yE2LUP7cF\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"915QBDW89m\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"1FBgxY0QQp\",\"data\":{\"text\":\"So the important question is not: Does this system have RAG?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4xdj86u8Qz\",\"data\":{\"text\":\"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"bGPa0AsJI6\",\"data\":{\"text\":\"Direct Answer\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"fBKcyJ0IcX\",\"data\":{\"text\":\"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"JbIPIJjkXK\",\"data\":{\"text\":\"In practical systems, a Retrieval Trigger can emerge from several conditions:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_JbTSHlrtH\",\"data\":{\"code\":\"Need for current information\\n        OR\\nNeed for exact source-specific information\\n        OR\\nNeed for evidence or provenance\\n        OR\\nNeed for private\u002Fuser-specific information\\n        OR\\nInsufficient knowledge coverage\\n        OR\\nConflicting evidence\\n        OR\\nHigh consequence of factual error\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Aaem6fQ_tF\",\"data\":{\"text\":\"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"x2DDg7Ue1-\",\"data\":{\"text\":\"Why This Is So\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1GTaWG9ViB\",\"data\":{\"text\":\"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"klwNY3lr1d\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Fyw2AVDbxR\",\"data\":{\"text\":\"This distinction creates an unavoidable systems problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4FvbthV3in\",\"data\":{\"text\":\"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"asxdihTbcB\",\"data\":{\"text\":\"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"jGgq116uAa\",\"data\":{\"text\":\"That point is where a Retrieval Trigger becomes useful.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T6q_BUeDg3\",\"data\":{\"text\":\"Context\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"9H_bNlyoYs\",\"data\":{\"text\":\"Traditional RAG often looks like this:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YSZR1AInSj\",\"data\":{\"code\":\"Question\\n↓\\nRetrieve documents\\n↓\\nAdd documents to context\\n↓\\nGenerate answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"HnzY2Q9xTs\",\"data\":{\"text\":\"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Aho03YTGAU\",\"data\":{\"text\":\"More advanced approaches introduce an adaptive step:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uK0l0tYLg6\",\"data\":{\"code\":\"Question\\n↓\\nEvaluate information requirement\\n↓\\n        ┌───────────────┐\\n        │               │\\n   no retrieval      retrieval\\n        │               │\\n        ↓               ↓\\n model knowledge    external evidence\\n        │               │\\n        └───────┬───────┘\\n                ↓\\n              answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Upb-15aN8T\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"I5hs5j9IKc\",\"data\":{\"text\":\"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"mw2jbuWA-g\",\"data\":{\"text\":\"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DUba0EfbWg\",\"data\":{\"text\":\"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wzX0jC8H8b\",\"data\":{\"text\":\"Assumptions\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4puAk8h-NF\",\"data\":{\"text\":\"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Qsm42lc7aC\",\"data\":{\"text\":\"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Rznt7yvqT2\",\"data\":{\"text\":\"It also assumes that retrieval has a cost. That cost does not have to be financial.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wQoEfZuFPe\",\"data\":{\"text\":\"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"WM1F9QkT2G\",\"data\":{\"text\":\"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Z4gw9SX7jo\",\"data\":{\"text\":\"Variables\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"Z_sKNO6vmp\",\"data\":{\"text\":\"A practical Retrieval Trigger can consider five primary variables.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Eti88tz1T6\",\"data\":{\"text\":\"Freshness\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"3zKe198lls\",\"data\":{\"text\":\"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ryQRR7TzC7\",\"data\":{\"text\":\"Specificity\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"bkXBBuCBb_\",\"data\":{\"text\":\"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"LlT6c-tPU2\",\"data\":{\"text\":\"Evidence Requirement\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1G-aWjGT1c\",\"data\":{\"text\":\"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lnoOCm4KDw\",\"data\":{\"text\":\"Knowledge Coverage\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"nKGrZO0Zw0\",\"data\":{\"text\":\"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SYp_4G0qXz\",\"data\":{\"text\":\"Consequence of Error\",\"level\":3},\"type\":\"header\",\"tunes\":{}},{\"id\":\"YJeo8nKsl9\",\"data\":{\"text\":\"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NTh27HJjo1\",\"data\":{\"text\":\"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"A25id0cm1s\",\"data\":{\"text\":\"Diagnostic \u002F Decision Method\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"az70f7cIIF\",\"data\":{\"text\":\"A very simple Retrieval Trigger can be implemented without machine learning.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"yUFgVx9VRM\",\"data\":{\"code\":\"def should_retrieve(\\n    time_sensitive=False,\\n    source_specific=False,\\n    evidence_required=False,\\n    private_context=False,\\n    knowledge_uncertain=False,\\n    conflicting_information=False\\n):\\n    return any([\\n        time_sensitive,\\n        source_specific,\\n        evidence_required,\\n        private_context,\\n        knowledge_uncertain,\\n        conflicting_information,\\n    ])\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tBn6sOGnKB\",\"data\":{\"text\":\"For a stable factual question:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"BW2rsTbqqL\",\"data\":{\"code\":\"should_retrieve()\\n# False\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"iriE0iq97f\",\"data\":{\"text\":\"For a current stock price:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"d10aolm-TW\",\"data\":{\"code\":\"should_retrieve(\\n    time_sensitive=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"nwL_vpUi-Y\",\"data\":{\"text\":\"For a scientific claim:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-DXgs4BBKH\",\"data\":{\"code\":\"should_retrieve(\\n    source_specific=True,\\n    evidence_required=True\\n)\\n# True\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"Wj1sAbZ7l8\",\"data\":{\"text\":\"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"loLbe4TkAK\",\"data\":{\"text\":\"The implementation can change. The architectural question remains the same:\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"T2PJWaSYp9\",\"data\":{\"text\":\"Is the evidence currently available to the model sufficient for the answer it is about to produce?\",\"caption\":\"\",\"alignment\":\"left\"},\"type\":\"quote\",\"tunes\":{}},{\"id\":\"9Alw1zzG4E\",\"data\":{\"text\":\"Evidence\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"OgqdUwG1J-\",\"data\":{\"text\":\"The concept proposed here is consistent with several lines of retrieval research.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"QxIkEDnlBu\",\"data\":{\"text\":\"The original \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nqdi_kDLE3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"UThAFgyEe3\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CT8n5F5KLR\",\"data\":{\"text\":\"\u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"RDjo1UGf2s\",\"data\":{\"text\":\"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"nmWZ-exi8b\",\"data\":{\"text\":\"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"8a-H_OlfG1\",\"data\":{\"text\":\"Real Examples\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"l0KONt5Buo\",\"data\":{\"text\":\"Consider a support assistant connected to a company's documentation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MOy11BOFq5\",\"data\":{\"code\":\"\\\"How do I reset my password?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"tsZcuMS1sT\",\"data\":{\"text\":\"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"l53NPB6WNV\",\"data\":{\"code\":\"\\\"What permissions does my account currently have?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"RKQXMbXA29\",\"data\":{\"text\":\"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"b5tNqxBKir\",\"data\":{\"code\":\"\\\"Why was my production deployment rejected yesterday?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"xaP7a7lV3i\",\"data\":{\"text\":\"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"SlBdofaCVq\",\"data\":{\"text\":\"The same logic works for web search.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"VgFaQjUMnU\",\"data\":{\"code\":\"\\\"What is RAG?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"6BG7aSJQzt\",\"data\":{\"text\":\"A general explanation may not require retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"TRngQB41uY\",\"data\":{\"code\":\"\\\"What did the authors of Self-RAG specifically conclude about unnecessary retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"7UIuDjIRyG\",\"data\":{\"text\":\"Now source-specific evidence is required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"CG2PbVS1yz\",\"data\":{\"code\":\"\\\"What is the latest research on adaptive retrieval?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"G1gyMnE_E8\",\"data\":{\"text\":\"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"NyJtHsPsSf\",\"data\":{\"text\":\"Common Misconceptions and Failure Modes\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"1otM6VenxR\",\"data\":{\"text\":\"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7BpMfX7lOZ\",\"data\":{\"text\":\"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"THz75XkfrR\",\"data\":{\"text\":\"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"gOUGv2dAaq\",\"data\":{\"text\":\"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Mz8i-je--k\",\"data\":{\"text\":\"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"imAEotM35y\",\"data\":{\"text\":\"Edge Cases\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8xkcG8hc9c\",\"data\":{\"text\":\"Some questions contain both stable and unstable information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"IYDiRezoWn\",\"data\":{\"code\":\"\\\"Who founded NVIDIA, and what is its market capitalization today?\\\"\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"2kxOM8vxYh\",\"data\":{\"text\":\"The first part may be answerable from stable model knowledge. The second part requires current information.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"6PyzlxURFS\",\"data\":{\"text\":\"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lsbZ8aQAD6\",\"data\":{\"text\":\"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"-Y67JvJusX\",\"data\":{\"text\":\"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"32VdDErqUM\",\"data\":{\"text\":\"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"edCyD-PqlU\",\"data\":{\"text\":\"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"rmjW0MBcFo\",\"data\":{\"code\":\"Retrieval Trigger\\n≠\\npermission to answer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"gZBq0voX0-\",\"data\":{\"text\":\"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"DmO9cFY93l\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"gV4YT_2O1X\",\"data\":{\"text\":\"The Retrieval Trigger is a conceptual framework, not a universal algorithm.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"7c6OA2X4-H\",\"data\":{\"text\":\"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Xn8K4ArjdA\",\"data\":{\"text\":\"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"y0gYRFZx6m\",\"data\":{\"text\":\"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Kcvx1v4Z1x\",\"data\":{\"text\":\"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"wcRpKvBhkb\",\"data\":{\"text\":\"The Retrieval Trigger therefore solves only one part of a larger architecture.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"YN1_g7vs7V\",\"data\":{\"text\":\"What Would Change This Answer?\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"4PYX_PYK_Z\",\"data\":{\"text\":\"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"vyqJ8Kp8Ye\",\"data\":{\"text\":\"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KN0MPs6as\",\"data\":{\"text\":\"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Zrlr4a0utJ\",\"data\":{\"text\":\"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"4hl7r5cF1M\",\"data\":{\"text\":\"The implementation may disappear from view. The architectural question remains.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"o_g5g_6eSj\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"L6MWm8xdAg\",\"data\":{\"text\":\"RAG begins too late to explain the whole problem.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"uymgoYlFM5\",\"data\":{\"text\":\"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"_KIblg0ae_\",\"data\":{\"code\":\"Stable known fact\\n→ answer from model knowledge\\n\\nCurrent fact\\n→ retrieve\\n\\nSource-specific or evidence-dependent claim\\n→ retrieve and verify\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"unCgfbeYI5\",\"data\":{\"text\":\"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"ZAosU4trn9\",\"data\":{\"code\":\"Model Knowledge\\n        ↓\\nRetrieval Trigger\\n        ↓\\nRuntime Knowledge \u002F RAG\\n        ↓\\nEvidence\\n        ↓\\nReasoning\\n        ↓\\nAnswer Validity Boundary\\n        ↓\\nAnswer\"},\"type\":\"code\",\"tunes\":{}},{\"id\":\"f0ZIysaJy1\",\"data\":{\"text\":\"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"iCg9ojv75m\",\"data\":{\"text\":\"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"cDBiNnZJv-\",\"data\":{\"text\":\"Primary Sources\",\"level\":2},\"type\":\"header\",\"tunes\":{}},{\"id\":\"8gumvODB16\",\"data\":{\"text\":\"Patrick Lewis et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\\\" target=\\\"_blank\\\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"Chz6I7zmlv\",\"data\":{\"text\":\"Zhengbao Jiang et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\\\" target=\\\"_blank\\\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"MRdjivpsoW\",\"data\":{\"text\":\"Akari Asai et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\\\" target=\\\"_blank\\\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.\"},\"type\":\"paragraph\",\"tunes\":{}},{\"id\":\"lck29euXJP\",\"data\":{\"text\":\"Soyeong Jeong et al., \u003Ca href=\\\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\\\" target=\\\"_blank\\\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.\"},\"type\":\"paragraph\",\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1096,"blocks":1097,"version":1737},1790574879391,[1098,1102,1106,1110,1114,1118,1122,1126,1131,1135,1139,1143,1147,1151,1155,1158,1162,1166,1170,1174,1178,1182,1201,1205,1208,1212,1216,1219,1223,1227,1230,1234,1238,1242,1246,1250,1254,1258,1262,1266,1270,1274,1278,1282,1286,1290,1293,1297,1301,1305,1309,1313,1317,1321,1325,1329,1333,1336,1340,1344,1347,1351,1355,1359,1363,1367,1371,1375,1379,1383,1387,1391,1395,1399,1403,1407,1411,1415,1419,1423,1427,1431,1435,1439,1443,1447,1450,1454,1457,1461,1464,1468,1471,1475,1479,1483,1487,1491,1495,1499,1503,1507,1511,1515,1519,1523,1526,1530,1533,1537,1540,1544,1548,1551,1555,1558,1562,1565,1569,1573,1577,1581,1585,1589,1593,1597,1601,1604,1608,1612,1616,1620,1624,1628,1631,1635,1639,1643,1647,1651,1655,1659,1663,1667,1671,1675,1679,1683,1687,1691,1695,1699,1702,1706,1709,1713,1717,1721,1725,1729,1733],{"id":215,"data":1099,"type":42,"tunes":1101},{"text":1100,"level":218},"Question",{},{"id":221,"data":1103,"type":224,"tunes":1105},{"text":1104},"When should an AI stop relying on what it already knows and retrieve external information before answering?",{},{"id":227,"data":1107,"type":224,"tunes":1109},{"text":1108},"This question appears simple, but it sits at the center of one of the most important design decisions in modern AI systems.",{},{"id":232,"data":1111,"type":224,"tunes":1113},{"text":1112},"Large language models contain substantial knowledge in their parameters. Retrieval-Augmented Generation adds external information at runtime. But neither extreme is ideal.",{},{"id":237,"data":1115,"type":224,"tunes":1117},{"text":1116},"Always trusting the model can produce outdated or unsupported answers. Always retrieving information adds latency, cost, irrelevant context and new opportunities for retrieval errors.",{},{"id":242,"data":1119,"type":224,"tunes":1121},{"text":1120},"The real problem therefore comes before RAG: When should retrieval happen at all?",{},{"id":247,"data":1123,"type":224,"tunes":1125},{"text":1124},"This article uses the term Retrieval Trigger for that decision. Retrieval Trigger is not presented here as a standardized term from the research literature. It is a practical systems concept that brings together ideas already visible in research on active, adaptive and self-reflective retrieval.",{},{"id":252,"data":1127,"type":257,"tunes":1130},{"text":1128,"caption":1129,"alignment":256},"A Retrieval Trigger is a condition indicating that an AI system should stop relying solely on internal model knowledge and obtain external evidence before producing or finalizing an answer.","Working definition",{},{"id":260,"data":1132,"type":264,"tunes":1134},{"title":1133,"maxLevel":263,"minLevel":218},"Contents",{},{"id":267,"data":1136,"type":42,"tunes":1138},{"text":1137,"level":218},"What This Really Means",{},{"id":272,"data":1140,"type":224,"tunes":1142},{"text":1141},"An LLM has two fundamentally different ways of obtaining information.",{},{"id":277,"data":1144,"type":224,"tunes":1146},{"text":1145},"The first is model knowledge. This is information represented in the model's learned parameters. No database query, web search or document lookup is required at runtime.",{},{"id":282,"data":1148,"type":224,"tunes":1150},{"text":1149},"The second is runtime knowledge. This is information provided while the model is operating: search results, database records, documents, APIs, user files, tool outputs or other retrieved evidence.",{},{"id":287,"data":1152,"type":224,"tunes":1154},{"text":1153},"RAG connects these two worlds. But RAG itself does not answer the question of when that connection should be activated. That is the purpose of the Retrieval Trigger.",{},{"id":292,"data":1156,"type":295,"tunes":1157},{"code":294},{},{"id":298,"data":1159,"type":224,"tunes":1161},{"text":1160},"The Retrieval Trigger therefore sits before retrieval. The Answer Validity Boundary sits later.",{},{"id":303,"data":1163,"type":224,"tunes":1165},{"text":1164},"The first asks: Do I need external evidence?",{},{"id":308,"data":1167,"type":224,"tunes":1169},{"text":1168},"The second asks: Do I now have enough evidence to support this answer?",{},{"id":313,"data":1171,"type":224,"tunes":1173},{"text":1172},"These are related decisions, but they are not the same decision.",{},{"id":318,"data":1175,"type":42,"tunes":1177},{"text":1176,"level":218},"Simplest Example",{},{"id":323,"data":1179,"type":224,"tunes":1181},{"text":1180},"Consider three questions.",{},{"id":328,"data":1183,"type":346,"tunes":1200},{"content":1184,"stretched":43,"withHeadings":14},[1185,1188,1192,1196],[1100,1186,1187],"Internal knowledge","Retrieval Trigger",[1189,1190,1191],"What is the capital of France?","Usually sufficient","No strong trigger",[1193,1194,1195],"What is the current NVIDIA stock price?","Potentially outdated","Trigger retrieval",[1197,1198,1199],"Does this new scientific paper prove that X causes Y?","Cannot establish the claim without examining the evidence","Strong retrieval trigger",{},{"id":349,"data":1202,"type":224,"tunes":1204},{"text":1203},"The first question is based on a highly stable fact.",{},{"id":354,"data":1206,"type":295,"tunes":1207},{"code":356},{},{"id":359,"data":1209,"type":224,"tunes":1211},{"text":1210},"Retrieving documents before answering would usually add little value.",{},{"id":364,"data":1213,"type":224,"tunes":1215},{"text":1214},"Now consider a question whose answer changes continuously.",{},{"id":369,"data":1217,"type":295,"tunes":1218},{"code":371},{},{"id":374,"data":1220,"type":224,"tunes":1222},{"text":1221},"The model may know a great deal about NVIDIA. That does not mean it knows the price now.",{},{"id":379,"data":1224,"type":224,"tunes":1226},{"text":1225},"The third example is even more important.",{},{"id":384,"data":1228,"type":295,"tunes":1229},{"code":386},{},{"id":389,"data":1231,"type":224,"tunes":1233},{"text":1232},"The model's reasoning capability may be perfectly useful. The missing component is evidence.",{},{"id":394,"data":1235,"type":224,"tunes":1237},{"text":1236},"That distinction is fundamental.",{},{"id":399,"data":1239,"type":42,"tunes":1241},{"text":1240,"level":218},"Where the Example Stops Working",{},{"id":404,"data":1243,"type":224,"tunes":1245},{"text":1244},"The examples above make the decision appear binary: retrieve or do not retrieve.",{},{"id":409,"data":1247,"type":224,"tunes":1249},{"text":1248},"Real systems are more complicated. A question may contain several claims, some stable and some current. Retrieved documents may disagree. A retriever may return irrelevant information. The relevant information may exist but fail to rank highly enough. A document may be authoritative but outdated.",{},{"id":414,"data":1251,"type":224,"tunes":1253},{"text":1252},"Retrieval itself can also introduce incorrect context into an otherwise reasonable answer.",{},{"id":419,"data":1255,"type":224,"tunes":1257},{"text":1256},"This is why retrieval should not be treated as an automatic synonym for truth.",{},{"id":424,"data":1259,"type":224,"tunes":1261},{"text":1260},"Research on adaptive retrieval has increasingly moved away from the assumption that every query should receive the same retrieval strategy.",{},{"id":429,"data":1263,"type":224,"tunes":1265},{"text":1264},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa>, for example, explicitly explores retrieval on demand rather than indiscriminately retrieving a fixed number of passages for every input. The authors discuss how unnecessary or irrelevant retrieval can reduce answer quality.",{},{"id":434,"data":1267,"type":224,"tunes":1269},{"text":1268},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> similarly selects between no retrieval, single-step retrieval and more complex retrieval strategies according to question complexity.",{},{"id":439,"data":1271,"type":224,"tunes":1273},{"text":1272},"So the important question is not: Does this system have RAG?",{},{"id":444,"data":1275,"type":224,"tunes":1277},{"text":1276},"It is: Can this system recognize when retrieval is necessary and what kind of retrieval is appropriate?",{},{"id":449,"data":1279,"type":42,"tunes":1281},{"text":1280,"level":218},"Direct Answer",{},{"id":454,"data":1283,"type":224,"tunes":1285},{"text":1284},"An AI should trigger retrieval when answering requires information that its internal model knowledge cannot safely provide with the required freshness, specificity, provenance or evidential support.",{},{"id":459,"data":1287,"type":224,"tunes":1289},{"text":1288},"In practical systems, a Retrieval Trigger can emerge from several conditions:",{},{"id":464,"data":1291,"type":295,"tunes":1292},{"code":466},{},{"id":469,"data":1294,"type":224,"tunes":1296},{"text":1295},"If none of these conditions is materially present, retrieval may be unnecessary. If one or more are present, external evidence becomes part of the answer-generation process.",{},{"id":474,"data":1298,"type":42,"tunes":1300},{"text":1299,"level":218},"Why This Is So",{},{"id":479,"data":1302,"type":224,"tunes":1304},{"text":1303},"A language model's internal knowledge is often described as parametric knowledge. It was learned during training and encoded into the model's parameters.",{},{"id":484,"data":1306,"type":224,"tunes":1308},{"text":1307},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Lewis et al.'s original RAG work\u003C\u002Fa> framed retrieval as a combination of this parametric memory with external, non-parametric memory. The external memory can be searched and updated without retraining the entire language model.",{},{"id":489,"data":1310,"type":224,"tunes":1312},{"text":1311},"This distinction creates an unavoidable systems problem.",{},{"id":494,"data":1314,"type":224,"tunes":1316},{"text":1315},"The model can know things. But the model cannot assume that everything it knows is current, complete, specific enough and supported by the required evidence.",{},{"id":499,"data":1318,"type":224,"tunes":1320},{"text":1319},"A model can therefore produce a linguistically convincing answer while still operating beyond the point where its internal knowledge is sufficient.",{},{"id":504,"data":1322,"type":224,"tunes":1324},{"text":1323},"That point is where a Retrieval Trigger becomes useful.",{},{"id":509,"data":1326,"type":42,"tunes":1328},{"text":1327,"level":218},"Context",{},{"id":514,"data":1330,"type":224,"tunes":1332},{"text":1331},"Traditional RAG often looks like this:",{},{"id":519,"data":1334,"type":295,"tunes":1335},{"code":521},{},{"id":524,"data":1337,"type":224,"tunes":1339},{"text":1338},"This architecture assumes retrieval before generation. That works well for many knowledge-intensive applications, but it can also perform unnecessary retrieval.",{},{"id":529,"data":1341,"type":224,"tunes":1343},{"text":1342},"More advanced approaches introduce an adaptive step:",{},{"id":534,"data":1345,"type":295,"tunes":1346},{"code":536},{},{"id":539,"data":1348,"type":224,"tunes":1350},{"text":1349},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> goes further by considering retrieval during generation itself. It uses upcoming generation and low-confidence tokens as signals for retrieving additional information.",{},{"id":544,"data":1352,"type":224,"tunes":1354},{"text":1353},"Self-RAG similarly introduces mechanisms allowing retrieval, generation and critique to interact instead of treating retrieval as an unconditional preprocessing step.",{},{"id":549,"data":1356,"type":224,"tunes":1358},{"text":1357},"Adaptive-RAG approaches the same broader problem from query complexity: different questions may require different retrieval strategies.",{},{"id":554,"data":1360,"type":224,"tunes":1362},{"text":1361},"These approaches differ technically. But they expose the same architectural insight: Retrieval should be a decision, not merely a permanent switch.",{},{"id":559,"data":1364,"type":42,"tunes":1366},{"text":1365,"level":218},"Assumptions",{},{"id":564,"data":1368,"type":224,"tunes":1370},{"text":1369},"The Retrieval Trigger framework assumes that a system has access to at least one external information source when retrieval is required.",{},{"id":569,"data":1372,"type":224,"tunes":1374},{"text":1373},"That source could be web search, a document store, vector database, SQL database, knowledge graph, API, enterprise system, user-uploaded document or tool output.",{},{"id":574,"data":1376,"type":224,"tunes":1378},{"text":1377},"It also assumes that retrieval has a cost. That cost does not have to be financial.",{},{"id":579,"data":1380,"type":224,"tunes":1382},{"text":1381},"Retrieval introduces latency, token consumption, context usage, infrastructure complexity and the possibility of retrieving misleading information.",{},{"id":584,"data":1384,"type":224,"tunes":1386},{"text":1385},"The optimal system therefore does not maximize retrieval. It maximizes appropriate retrieval.",{},{"id":589,"data":1388,"type":42,"tunes":1390},{"text":1389,"level":218},"Variables",{},{"id":594,"data":1392,"type":224,"tunes":1394},{"text":1393},"A practical Retrieval Trigger can consider five primary variables.",{},{"id":599,"data":1396,"type":42,"tunes":1398},{"text":1397,"level":263},"Freshness",{},{"id":604,"data":1400,"type":224,"tunes":1402},{"text":1401},"How likely is the required information to have changed? The capital of France has very low volatility. A stock price has extremely high volatility.",{},{"id":609,"data":1404,"type":42,"tunes":1406},{"text":1405,"level":263},"Specificity",{},{"id":614,"data":1408,"type":224,"tunes":1410},{"text":1409},"Does the question require information from a particular source, document, organization, account or dataset? If the user asks what a specific contract says, general model knowledge is irrelevant. The contract must be retrieved.",{},{"id":619,"data":1412,"type":42,"tunes":1414},{"text":1413,"level":263},"Evidence Requirement",{},{"id":624,"data":1416,"type":224,"tunes":1418},{"text":1417},"Does the answer need provenance? A model may know that a claim is generally accepted but still need a source when the task requires verification.",{},{"id":629,"data":1420,"type":42,"tunes":1422},{"text":1421,"level":263},"Knowledge Coverage",{},{"id":634,"data":1424,"type":224,"tunes":1426},{"text":1425},"Is the subject likely to be represented adequately in internal model knowledge? Rare, proprietary, highly local or newly published information creates stronger retrieval pressure.",{},{"id":639,"data":1428,"type":42,"tunes":1430},{"text":1429,"level":263},"Consequence of Error",{},{"id":644,"data":1432,"type":224,"tunes":1434},{"text":1433},"Not every incorrect answer has the same impact. Where factual accuracy materially affects a decision, the acceptable evidence threshold may be higher.",{},{"id":649,"data":1436,"type":224,"tunes":1438},{"text":1437},"These variables do not have to be implemented as literal numeric scores. They describe the decision surface.",{},{"id":654,"data":1440,"type":42,"tunes":1442},{"text":1441,"level":218},"Diagnostic \u002F Decision Method",{},{"id":659,"data":1444,"type":224,"tunes":1446},{"text":1445},"A very simple Retrieval Trigger can be implemented without machine learning.",{},{"id":664,"data":1448,"type":295,"tunes":1449},{"code":666},{},{"id":669,"data":1451,"type":224,"tunes":1453},{"text":1452},"For a stable factual question:",{},{"id":674,"data":1455,"type":295,"tunes":1456},{"code":676},{},{"id":679,"data":1458,"type":224,"tunes":1460},{"text":1459},"For a current stock price:",{},{"id":684,"data":1462,"type":295,"tunes":1463},{"code":686},{},{"id":689,"data":1465,"type":224,"tunes":1467},{"text":1466},"For a scientific claim:",{},{"id":694,"data":1469,"type":295,"tunes":1470},{"code":696},{},{"id":699,"data":1472,"type":224,"tunes":1474},{"text":1473},"Production systems can make this decision far more sophisticated. A classifier could predict retrieval requirements. A model could emit special control tokens. A router could classify query complexity. Retrieval could also be triggered repeatedly during generation.",{},{"id":704,"data":1476,"type":224,"tunes":1478},{"text":1477},"The implementation can change. The architectural question remains the same:",{},{"id":709,"data":1480,"type":257,"tunes":1482},{"text":1481,"caption":712,"alignment":256},"Is the evidence currently available to the model sufficient for the answer it is about to produce?",{},{"id":715,"data":1484,"type":42,"tunes":1486},{"text":1485,"level":218},"Evidence",{},{"id":720,"data":1488,"type":224,"tunes":1490},{"text":1489},"The concept proposed here is consistent with several lines of retrieval research.",{},{"id":725,"data":1492,"type":224,"tunes":1494},{"text":1493},"The original \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">RAG architecture\u003C\u002Fa> demonstrated the usefulness of combining parametric model knowledge with external non-parametric knowledge, particularly for knowledge-intensive tasks.",{},{"id":730,"data":1496,"type":224,"tunes":1498},{"text":1497},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">FLARE\u003C\u002Fa> explicitly explores active retrieval during generation, including retrieval prompted by low-confidence upcoming content.",{},{"id":735,"data":1500,"type":224,"tunes":1502},{"text":1501},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG\u003C\u002Fa> demonstrates an architecture in which retrieval can occur on demand and is followed by reflection on retrieved passages and generated content.",{},{"id":740,"data":1504,"type":224,"tunes":1506},{"text":1505},"\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG\u003C\u002Fa> dynamically chooses among different strategies according to question complexity, including situations where no retrieval is required.",{},{"id":745,"data":1508,"type":224,"tunes":1510},{"text":1509},"The term Retrieval Trigger is used here as a system-level abstraction over this broader family of decisions.",{},{"id":750,"data":1512,"type":224,"tunes":1514},{"text":1513},"It does not claim that these papers use the same terminology. Instead, it identifies the shared architectural problem: What causes an AI system to transition from internal knowledge to external evidence?",{},{"id":755,"data":1516,"type":42,"tunes":1518},{"text":1517,"level":218},"Real Examples",{},{"id":760,"data":1520,"type":224,"tunes":1522},{"text":1521},"Consider a support assistant connected to a company's documentation.",{},{"id":765,"data":1524,"type":295,"tunes":1525},{"code":767},{},{"id":770,"data":1527,"type":224,"tunes":1529},{"text":1528},"If the procedure is stable and reliably represented in the assistant's current instructions, direct answering may be appropriate.",{},{"id":775,"data":1531,"type":295,"tunes":1532},{"code":777},{},{"id":780,"data":1534,"type":224,"tunes":1536},{"text":1535},"That information is user-specific and dynamic. The Retrieval Trigger fires. The system must inspect the actual account or authorization data.",{},{"id":785,"data":1538,"type":295,"tunes":1539},{"code":787},{},{"id":790,"data":1541,"type":224,"tunes":1543},{"text":1542},"The model can understand deployment systems and explain common reasons. But the question is asking about a particular event. Logs, CI\u002FCD output or incident records are required.",{},{"id":795,"data":1545,"type":224,"tunes":1547},{"text":1546},"The same logic works for web search.",{},{"id":800,"data":1549,"type":295,"tunes":1550},{"code":802},{},{"id":805,"data":1552,"type":224,"tunes":1554},{"text":1553},"A general explanation may not require retrieval.",{},{"id":810,"data":1556,"type":295,"tunes":1557},{"code":812},{},{"id":815,"data":1559,"type":224,"tunes":1561},{"text":1560},"Now source-specific evidence is required.",{},{"id":820,"data":1563,"type":295,"tunes":1564},{"code":822},{},{"id":825,"data":1566,"type":224,"tunes":1568},{"text":1567},"This introduces a freshness requirement as well. The underlying subject has not changed. The information requirement has.",{},{"id":830,"data":1570,"type":42,"tunes":1572},{"text":1571,"level":218},"Common Misconceptions and Failure Modes",{},{"id":835,"data":1574,"type":224,"tunes":1576},{"text":1575},"More retrieval automatically produces a better answer. It does not. Irrelevant documents consume context and can distract generation.",{},{"id":840,"data":1578,"type":224,"tunes":1580},{"text":1579},"High model confidence means retrieval is unnecessary. A model can produce an incorrect answer confidently. Self-reported confidence should therefore not be treated as the only trigger.",{},{"id":845,"data":1582,"type":224,"tunes":1584},{"text":1583},"Successful retrieval means the answer is verified. Retrieval only provides candidate evidence. The evidence must still be relevant, sufficiently authoritative and correctly interpreted.",{},{"id":850,"data":1586,"type":224,"tunes":1588},{"text":1587},"RAG automatically solves outdated knowledge. It only does so if the retrieval corpus itself contains current information. Retrieving an outdated document does not create a current answer.",{},{"id":855,"data":1590,"type":224,"tunes":1592},{"text":1591},"One retrieval step is always enough. Complex questions may require several pieces of evidence or iterative retrieval.",{},{"id":860,"data":1594,"type":42,"tunes":1596},{"text":1595,"level":218},"Edge Cases",{},{"id":865,"data":1598,"type":224,"tunes":1600},{"text":1599},"Some questions contain both stable and unstable information.",{},{"id":870,"data":1602,"type":295,"tunes":1603},{"code":872},{},{"id":875,"data":1605,"type":224,"tunes":1607},{"text":1606},"The first part may be answerable from stable model knowledge. The second part requires current information.",{},{"id":880,"data":1609,"type":224,"tunes":1611},{"text":1610},"A sufficiently capable system should not necessarily treat the entire query as one retrieval decision. It can trigger retrieval only where required.",{},{"id":885,"data":1613,"type":224,"tunes":1615},{"text":1614},"Another edge case is disagreement between sources. Suppose retrieval returns three documents making incompatible claims.",{},{"id":890,"data":1617,"type":224,"tunes":1619},{"text":1618},"The Retrieval Trigger has already succeeded: the system recognized that external evidence was required. But the task is not finished.",{},{"id":895,"data":1621,"type":224,"tunes":1623},{"text":1622},"The system has now reached an evidence evaluation problem. This is where the Answer Validity Boundary becomes important.",{},{"id":900,"data":1625,"type":224,"tunes":1627},{"text":1626},"The system may have retrieved information and still not possess enough evidence to make a strong conclusion.",{},{"id":905,"data":1629,"type":295,"tunes":1630},{"code":907},{},{"id":910,"data":1632,"type":224,"tunes":1634},{"text":1633},"The trigger obtains evidence. The validity boundary determines whether that evidence is sufficient.",{},{"id":915,"data":1636,"type":42,"tunes":1638},{"text":1637,"level":218},"Limitations",{},{"id":920,"data":1640,"type":224,"tunes":1642},{"text":1641},"The Retrieval Trigger is a conceptual framework, not a universal algorithm.",{},{"id":925,"data":1644,"type":224,"tunes":1646},{"text":1645},"Different systems will require different trigger rules. A customer-support bot, scientific research assistant, search engine and autonomous software agent do not have identical evidence requirements.",{},{"id":930,"data":1648,"type":224,"tunes":1650},{"text":1649},"Trigger thresholds can also create their own failure modes. A threshold that is too low causes excessive retrieval. A threshold that is too high causes unsupported answering.",{},{"id":935,"data":1652,"type":224,"tunes":1654},{"text":1653},"The retrieval infrastructure itself also matters. A perfect trigger connected to a poor source collection still produces poor evidence.",{},{"id":940,"data":1656,"type":224,"tunes":1658},{"text":1657},"Similarly, an excellent knowledge base provides little value if the trigger never activates when it is needed.",{},{"id":945,"data":1660,"type":224,"tunes":1662},{"text":1661},"The Retrieval Trigger therefore solves only one part of a larger architecture.",{},{"id":950,"data":1664,"type":42,"tunes":1666},{"text":1665,"level":218},"What Would Change This Answer?",{},{"id":955,"data":1668,"type":224,"tunes":1670},{"text":1669},"Future models may contain better mechanisms for identifying their own knowledge limitations. Retrievers may become cheaper and faster. Long-context systems may carry far more source material continuously.",{},{"id":960,"data":1672,"type":224,"tunes":1674},{"text":1673},"Models may also increasingly combine search, databases, tools and structured knowledge without exposing a distinct RAG stage to the application developer.",{},{"id":965,"data":1676,"type":224,"tunes":1678},{"text":1677},"These changes could alter how the trigger is implemented. They do not necessarily remove the underlying decision.",{},{"id":970,"data":1680,"type":224,"tunes":1682},{"text":1681},"As long as there is a difference between information already available to the model and information that must be obtained externally, a system still needs some mechanism for determining when to cross that boundary.",{},{"id":975,"data":1684,"type":224,"tunes":1686},{"text":1685},"The implementation may disappear from view. The architectural question remains.",{},{"id":980,"data":1688,"type":42,"tunes":1690},{"text":1689,"level":218},"Conclusion",{},{"id":985,"data":1692,"type":224,"tunes":1694},{"text":1693},"RAG begins too late to explain the whole problem.",{},{"id":990,"data":1696,"type":224,"tunes":1698},{"text":1697},"Before retrieval can happen, an AI system must determine whether retrieval is necessary. That decision is the Retrieval Trigger.",{},{"id":995,"data":1700,"type":295,"tunes":1701},{"code":997},{},{"id":1000,"data":1703,"type":224,"tunes":1705},{"text":1704},"But the broader implication is more important. Reliable AI does not merely need access to knowledge. It needs a method for determining when its current knowledge is insufficient.",{},{"id":1005,"data":1707,"type":295,"tunes":1708},{"code":1007},{},{"id":1010,"data":1710,"type":224,"tunes":1712},{"text":1711},"The Retrieval Trigger determines when the system should seek evidence. The Answer Validity Boundary determines whether that evidence is sufficient.",{},{"id":1015,"data":1714,"type":224,"tunes":1716},{"text":1715},"Together they describe something more useful than RAG alone: a decision process for moving from what an AI appears to know toward what it can actually support.",{},{"id":1020,"data":1718,"type":42,"tunes":1720},{"text":1719,"level":218},"Primary Sources",{},{"id":1025,"data":1722,"type":224,"tunes":1724},{"text":1723},"Patrick Lewis et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401\" target=\"_blank\">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u003C\u002Fa> (2020). Foundational RAG work describing the combination of parametric model memory with external non-parametric memory.",{},{"id":1030,"data":1726,"type":224,"tunes":1728},{"text":1727},"Zhengbao Jiang et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06983\" target=\"_blank\">Active Retrieval Augmented Generation\u003C\u002Fa> (2023). Introduces FLARE and active retrieval during generation, including retrieval based on low-confidence predicted content.",{},{"id":1035,"data":1730,"type":224,"tunes":1732},{"text":1731},"Akari Asai et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11511\" target=\"_blank\">Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\u003C\u002Fa> (2023). Explores adaptive retrieval on demand and self-reflection instead of unconditional fixed retrieval.",{},{"id":1040,"data":1734,"type":224,"tunes":1736},{"text":1735},"Soyeong Jeong et al., \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14403\" target=\"_blank\">Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity\u003C\u002Fa> (2024). Dynamically selects among no retrieval, single-step retrieval and more complex retrieval strategies according to the incoming question.",{},"2.31.6","An AI model does not need retrieval for every question. The important problem is knowing when its internal knowledge is no longer enough. The Retrieval Trigger is a practical decision boundary that determines when an AI system should stop relying solely on model knowledge and obtain external evidence before 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Ova dijagnostička metoda izoluje pokrivenost izvora, konstrukciju upita, pretragu, rangiranje, sastavljanje konteksta, generisanje, pripisivanje dokaza i svežinu—tako da se stvarni kvar može reprodukovati i ispraviti.","\u002Fuploads\u002F2026\u002F09\u002Frag-failed-but-which-layer-actually-failed-a-diagnostic-method-1790350847177-pior4c.webp","2026-09-24T19:39:00.000Z",{"id":2304,"slug":2305,"title":2306,"excerpt":2307,"featuredImage":2308,"publishedAt":2309},"466","the-gpu-is-not-the-product-future-proof-private-ai-architecture","GPU nije proizvod: Privatna AI arhitektura spremna za budućnost","Privatna AI infrastruktura ne bi trebalo da bude projektovana oko jednog GPU-a ili jednog modela. Otporniji pristup kombinuje brze GPU-ove za inferenciju, memorijski bogate AI sisteme, čvorove za fizički AI i opcione vodeće modele u oblaku iza sloja za rutiranje koji prepoznaje mogućnosti.","\u002Fuploads\u002F2026\u002F09\u002Fthe-gpu-is-not-the-product-future-proof-private-ai-architecture-1790140878812-8hsl39.webp","2026-09-23T01:19:00.000Z",{"id":2311,"slug":2312,"title":2313,"excerpt":2314,"featuredImage":2315,"publishedAt":2316},"467","the-answer-validity-boundary-the-missing-layer-between-relevance-and-reliable-ai-answers","Granica valjanosti odgovora: Nedostajući sloj između relevantnosti i pouzdanih AI odgovora","Izvor može biti relevantan, autoritativan i ipak pogrešan za pitanje koje se postavlja. Sloj koji nedostaje je primenljivost: uslovi pod kojima odgovor važi i promene koje ga primoravaju na preispitivanje. Ovaj članak predstavlja Granicu važenja odgovora kao obrazac za dizajn izvora za ljude, AI pretragu i RAG sisteme.","\u002Fuploads\u002F2026\u002F09\u002Fthe-answer-validity-boundary-the-missing-layer-between-relevance-and-reliable-ai-answers-1790272901306-1g5jly.webp","2026-09-24T11:59:00.000Z","fallback",[],[]]