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дигитализацију","\u002Fportfolio\u002Fdigitalisierungsportal-archiv-museum-bibliothek-ead-lido-mets-mods",[],{"statusCode":4,"data":206,"message":1609},{"id":207,"title":208,"slug":209,"content":210,"contentJson":211,"excerpt":789,"featuredImage":790,"featuredImageAlt":791,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":792,"publishedAt":793,"createdAt":794,"updatedAt":795,"seoLocalePaths":796,"categories":805,"author":806,"translations":810},"462","Prompt je deo pristrasnosti: Kako AI uokviravanje oblikuje rezonovanje","the-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u003Cp>Prompt se obično posmatra kao instrukcija poslata AI modelu: opišite zadatak dovoljno jasno, pružite neophodan kontekst, i model bi trebalo da pruži bolji odgovor.\u003C\u002Fp>\n\u003Cp>Taj model je nepotpun.\u003C\u002Fp>\n\u003Cp>Prompt ne opisuje samo problem. On takođe učestvuje u izgradnji okruženja za rezonovanje u okviru koga se problem procenjuje. Njegov izbor reči, redosled, pretpostavke, oznake, primeri, tražena perspektiva, pa čak i izneto uverenje korisnika mogu uticati na krajnji rezultat.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Prompt se ne nalazi izvan procesa rezonovanja. On je jedna od varijabli unutar njega.\u003C\u002Fblockquote>\n\u003Cp>Ova razlika je ključna za metodologiju predstavljenu u \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. Ako je cilj pouzdano rezonovanje, a ne samo korisno generisanje teksta, sam prompt se mora posmatrati kao mogući izvor metodološke pristrasnosti.\u003C\u002Fp>\n\u003Ch2>Kvalitet prompta i neutralnost prompta su različiti problemi\u003C\u002Fh2>\n\u003Cp>Prompt inženjering obično nastoji da poboljša jasnoću. Dvosmisleni zahtevi se zamenjuju eksplicitnim zahtevima. Dodaje se relevantan kontekst. Definišu se željene strukture izlaza. Ograničenja postaju vidljiva.\u003C\u002Fp>\n\u003Cp>Sve ovo može poboljšati kvalitet odgovora.\u003C\u002Fp>\n\u003Cp>Ali detaljniji prompt nije automatski i neutralniji prompt. Zapravo, dodatni detalji mogu uneti dodatne pretpostavke. Pažljivo osmišljen prompt može toliko efikasno ograničiti model da on pruži odličan odgovor na pitanje čije osnovno uokviravanje nikada nije dovedeno u pitanje.\u003C\u002Fp>\n\u003Cp>To su, stoga, dva različita cilja optimizacije:\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Kvalitet prompta:\u003C\u002Fb> da li model dobro razume i izvršava traženi zadatak?\u003C\u002Fli>\u003Cli>\u003Cb>Epistemička robusnost:\u003C\u002Fb> da li bi suštinski zaključak ostao odbranjiv ako bi se promenile upitne pretpostavke ili izbori uokviravanja u promptu?\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Prompt može dobiti visoku ocenu po prvom kriterijumu, a da se pritom loše pokaže po drugom.\u003C\u002Fp>\n\u003Ch2>Ne postoji savršeno neutralan prompt\u003C\u002Fh2>\n\u003Cp>Svaki netrivijalan prompt pravi izbore.\u003C\u002Fp>\n\u003Cp>On odlučuje koje su informacije uključene, a koje izostavljene. Određuje koji se koncepti imenuju. Može definisati potencijalna objašnjenja pre nego što model uopšte ispita dokaze. Uspostavlja terminologiju, opseg i fokus. U uporednim zadacima može odrediti koja se opcija pojavljuje prva. U istraživačkim zadacima može navesti pretpostavljeni odnos pre nego što postavi pitanje da li taj odnos uopšte postoji.\u003C\u002Fp>\n\u003Cp>Ove odluke ne čine odgovor nužno nevažećim. One, međutim, znače da se prompt ne može automatski posmatrati kao transparentan kanal kroz koji inače nezavisan proces rezonovanja posmatra problem.\u003C\u002Fp>\n\u003Cp>Brucks i Toubia opisuju srodan fenomen kao \u003Ci>arhitekturu prompta\u003C\u002Fi>. Kroz eksperimente velikih razmera koji su uključivali GPT-3, GPT-4 i Llama 3.1, otkrili su da naizgled sekundarni izbori u dizajnu — uključujući redosled odgovora, labele, uokviravanje i zahteve za obrazloženjem — mogu sistematski uticati na rezultate modela.\u003C\u002Fp>\n\u003Cp>Ovo je važno zato što ovi efekti nisu bili ograničeni samo na očigledno manipulativne prompte. Neke od uticajnih karakteristika bile su uobičajene strukturalne odluke koje korisnik mora da donese svaki put kada formuliše pitanje.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Odsustvo namere da se model pristrasno usmeri ne podrazumeva odsustvo efekta uokviravanja.\u003C\u002Fblockquote>\n\u003Ch2>Konkretan primer: Redosled i oznake\u003C\u002Fh2>\n\u003Cp>Problem arhitekture upita postaje lakše uočljiv u zadacima kontrolisanog poređenja.\u003C\u002Fp>\n\u003Cp>U jednom od eksperimenata koje su opisali Bruks (Brucks) i Tubija (Toubia), od GPT-4 je zatraženo da uporedi grupe stavki pod sistematski variranim uslovima upita. Nakon što su nepotpuni odgovori isključeni, studija je analizirala 5.447 zapažanja.\u003C\u002Fp>\n\u003Cp>U uslovima u kojima bi nepristrasan ispitanik trebalo podjednako često da odabere bilo koju poziciju odgovora, GPT-4 je izabrao prvu opciju u 63,21% zapažanja. Takođe je izabrao opciju označenu sa B umesto opcije označene sa C u 74,27% zapažanja.\u003C\u002Fp>\n\u003Cp>Te brojke se ne smeju generalizovati u tvrdnje poput 'GPT-4 uvek preferira prvu opciju' ili 'svi jezički modeli preferiraju B'. One opisuju specifične eksperimentalne uslove. Njihov metodološki značaj leži drugde: proizvoljna svojstva upita merljivo su promenila odgovore modela.\u003C\u002Fp>\n\u003Cp>Ista studija je takođe otkrila da prosto obaveštavanje modela da su redosled i oznake nasumično raspoređeni nije pouzdano otklonilo ove efekte. Različite strategije ublažavanja imale su različite efekte na različite modele.\u003C\u002Fp>\n\u003Cp>To zapažanje ima direktne posledice po ozbiljnu analizu potpomognutu veštačkom inteligencijom: reći modelu da 'bude nepristrasan' nije ekvivalentno kontrolisanju pristrasnosti.\u003C\u002Fp>\n\u003Ch2>Uokvirivanje je više od izbora reči\u003C\u002Fh2>\n\u003Cp>Najuticajniji efekti uokvirivanja često nisu pojedinačne reči. Oni su ugrađeni u logičku strukturu samog zahteva.\u003C\u002Fp>\n\u003Cp>Razmotrite razliku između ovih pitanja:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Koji dokazi podržavaju istorijski prenos između tradicije A i tradicije B?\u003C\u002Fblockquote>\n\u003Cp>i:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Kakav odnos, ako uopšte postoji, između tradicije A i tradicije B najbolje potkrepljuju dostupni dokazi?\u003C\u002Fblockquote>\n\u003Cp>Prva formulacija već pretpostavlja da hipoteza o prenosu zaslužuje pažnju modela. Ona traži dokaze unutar te hipoteze. Druga formulacija omogućava da se direktan prenos, indirektni uticaj, konvergencija, retrospektivna interpretacija ili odsustvo dokazivog odnosa ravnopravno takmiče pre nego što se donese zaključak.\u003C\u002Fp>\n\u003Cp>Činjenični korpus može biti identičan. Prostor pretrage nije.\u003C\u002Fp>\n\u003Cp>Ova razlika se može primeniti daleko izvan istorijskih istraživanja.\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cb>Otklanjanje grešaka:\u003C\u002Fb> 'Zašto nginx izaziva ovaj timeout?' uokviruje nginx kao uzročni cilj pre nego što su testirani alternativni uzroci.\u003C\u002Fli>\u003Cli>\u003Cb>Arhitektura:\u003C\u002Fb> 'Kako bi trebalo da implementiramo ovo koristeći mikroservise?' može potisnuti fundamentalnije pitanje da li su mikroservisi uopšte opravdani.\u003C\u002Fli>\u003Cli>\u003Cb>Strategija proizvoda:\u003C\u002Fb> 'Kako bi trebalo da plasiramo ovaj proizvod?' pretpostavlja da je lansiranje trenutno ispravan korak.\u003C\u002Fli>\u003Cli>\u003Cb>Bezbednost:\u003C\u002Fb> 'Kako je napadač ušao kroz API?' može usmeriti istragu ka putanji napada koja još uvek nije dokazana.\u003C\u002Fli>\u003Cli>\u003Cb>Istraživanje:\u003C\u002Fb> 'Šta objašnjava ovu korelaciju?' može podstaći uzročne narative čak i kada dostupni dokazi pokazuju samo povezanost.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Sofisticirani model može primetiti i dovesti u pitanje ove pretpostavke. Ali metodologija se ne može oslanjati na nadu da će to uvek činiti.\u003C\u002Fp>\n\u003Ch2>Praćenje uputstava stvara strukturalnu tenziju\u003C\u002Fh2>\n\u003Cp>Savremeni AI asistenti se namerno obučavaju da prate uputstva. Ta sposobnost je jedan od razloga zašto su korisni.\u003C\u002Fp>\n\u003Cp>Ali praćenje uputstava unosi važnu tenziju u analitički rad.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Od modela se istovremeno očekuje da poštuje definiciju zadatka koju je zadao korisnik i da nezavisno rasuđuje o tome da li ta definicija sadrži upitne pretpostavke.\u003C\u002Fblockquote>\n\u003Cp>Ovi ciljevi su obično kompatibilni. Ponekad nisu.\u003C\u002Fp>\n\u003Cp>Ako korisnik kaže: 'Pokaži mi zašto je arhitektura A superiornija u odnosu na arhitekturu B', visoko popustljiv odgovor može proizvesti ubedljiv argument za A. Taj odgovor može savršeno zadovoljiti uputstvo, a da pritom ne utvrdi da li je A zaista superiornija pod relevantnim ograničenjima.\u003C\u002Fp>\n\u003Cp>Zbog toga metodološko formulisanje upita mora praviti razliku između \u003Ci>izvršavanja dodeljenog argumentativnog zadatka\u003C\u002Fi> i \u003Ci>utvrđivanja koji zaključak ima najbolju potporu\u003C\u002Fi>.\u003C\u002Fp>\n\u003Cp>Prvo je legitimna sposobnost veštačke inteligencije. Ne treba je mešati sa drugim.\u003C\u002Fp>\n\u003Ch2>Sikofantija je povezana — ali to nije isti problem\u003C\u002Fh2>\n\u003Cp>Pristrasnost izazvana upitom se ponekad razmatra zajedno sa \u003Ci>sikofantijom\u003C\u002Fi>. Ovi koncepti se preklapaju, ali ih ne treba svoditi na jedno te isto.\u003C\u002Fp>\n\u003Cp>Efekti uokvirivanja opisuju promene u izlazu izazvane karakteristikama načina na koji je zadatak predstavljen. Sikofantija se konkretnije odnosi na ponašanje modela koje prekomerno udovoljava, potvrđuje ili se slaže sa navedenim stavom, preferencijama ili uverenjima korisnika.\u003C\u002Fp>\n\u003Cp>Efekat uokvirivanja se može javiti i bez prisustva mišljenja korisnika. Sam redosled opcija može uticati na rezultat. Nasuprot tome, sikofantija postaje relevantna kada sam stav korisnika utiče na odgovor.\u003C\u002Fp>\n\u003Cp>Istraživanje koje su sproveli Šarma i kolege otkrilo je sikofantsko ponašanje kod više AI asistenata i izvestilo da odgovori koji odgovaraju izraženim stavovima korisnika mogu dobiti jače signale ljudske naklonosti čak i kada su ti odgovori bili manje istiniti.\u003C\u002Fp>\n\u003Cp>Problem nije samo teorijski. U aprilu 2025. godine, OpenAI je javno povukao ažuriranje za GPT-4o nakon što je identifikovao ponašanje koje je opisao kao preterano laskavo ili popustljivo. Kompanija je navela da su pokušaji da se poboljšaju intuitivnost i efikasnost doveli do neželjenog porasta sikofantije.\u003C\u002Fp>\n\u003Cp>Kasnija zajednička procena usklađenosti kompanija Anthropic i OpenAI takođe je izvestila o poznatim oblicima sikofantije kod evaluiranih modela obe organizacije.\u003C\u002Fp>\n\u003Cp>Ove nalaze ne treba tumačiti kao dokaz da se AI asistenti uvek slažu sa svojim korisnicima. Oni pokazuju da je slaganje usklađeno sa korisnikom stvaran problem u ponašanju modela koji zahteva eksplicitnu procenu.\u003C\u002Fp>\n\u003Ch2>Korisnik može postati deo hipoteze\u003C\u002Fh2>\n\u003Cp>Problem postaje posebno suptilan u dugim analitičkim razgovorima.\u003C\u002Fp>\n\u003Cp>Kroz više koraka u razgovoru, korisnik može otkriti šta očekuje da pronađe, koja objašnjenja smatra uverljivim, koje dokaze vrednuje i kom zaključku trenutno daje prednost. Ovaj kontekst je koristan. Može dramatično poboljšati relevantnost rada modela.\u003C\u002Fp>\n\u003Cp>Ali to takođe znači da model više ne procenjuje samo spoljni problem. On procenjuje problem unutar okruženja razgovora koje sadrži očekivanja istraživača.\u003C\u002Fp>\n\u003Cp>U suštini, hipoteza korisnika može postati deo ulazne distribucije modela.\u003C\u002Fp>\n\u003Cp>To ne čini personalizaciju nepoželjnom. To znači da personalizacija i nezavisna provera služe različitim svrhama i da ih ponekad treba razdvojiti.\u003C\u002Fp>\n\u003Ch2>Problem se ne rešava traženjem od modela da se ne slaže\u003C\u002Fh2>\n\u003Cp>Prirodna reakcija jeste dodavanje uputstava kao što su:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Nemoj se slagati sa mnom. Budi kritičan. Preispitaj moje pretpostavke.\u003C\u002Fblockquote>\n\u003Cp>Ova uputstva mogu biti korisna, ali ne rešavaju metodološki problem.\u003C\u002Fp>\n\u003Cp>Slepo neslaganje nema veću epistemološku vrednost od slepog slaganja. Model optimizovan da se suprotstavlja korisniku može jednostavno uneti obrnutu pristrasnost.\u003C\u002Fp>\n\u003Cp>Cilj nije neslaganje.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Cilj je nezavisnost zaključka: dokazi treba da odrede da li je hipoteza korisnika ojačana, oslabljena, zamenjena ili ostala nerešena.\u003C\u002Fblockquote>\n\u003Cp>To zahteva proces u kojem se alternativna objašnjenja generišu zato što su uverljiva, a ne zato što je modelu naloženo da bude antagonističan.\u003C\u002Fp>\n\u003Ch2>Pristrasnost upita i pristrasnost potvrđivanja mogu se međusobno pojačavati\u003C\u002Fh2>\n\u003Cp>Uokvirivanje upita dobija veće posledice kada stupi u interakciju sa još jednim problemom u rasuđivanju: pristrasnošću potvrđivanja.\u003C\u002Fp>\n\u003Cp>Studija iz 2026. godine koju su sproveli Jhaveri i saradnici testirala je jedanaest jezičkih modela koristeći interaktivne zadatke otkrivanja pravila. Modeli su često predlagali testove koji su potvrđivali njihovu trenutnu hipotezu, umesto testova sposobnih da je opovrgnu. Eksplicitne intervencije koje podstiču formulisanje kontraprimera smanjile su ovo ponašanje i povećale prosečan učinak u otkrivanju pravila u izveštenim eksperimentima.\u003C\u002Fp>\n\u003Cp>Ovo stvara petlju koja se potencijalno sama pojačava:\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Korisničko uokvirivanje → početna hipoteza modela → potraga za potvrdnim dokazima → jače poverenje u uokvirenu hipotezu\u003C\u002Fblockquote>\n\u003Cp>Važno je istaći da svaki pojedinačni korak može delovati razumno. Slabost leži u strukturi samog procesa.\u003C\u002Fp>\n\u003Cp>Iz tog razloga, sledeći metodološki sloj ne može se svesti na to da se od modela samo traži više dokaza. On mora promeniti način na koji se postupa sa suprotstavljenim hipotezama i opovrgavajućim dokazima.\u003C\u002Fp>\n\u003Cp>Taj mehanizam je zasebno razvijen u tekstu \u003Ca href=\"{{STAJIC_ARTICLE_4_URL}}\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>Zašto više rezonovanja ne uklanja problem automatski\u003C\u002Fh2>\n\u003Cp>Primamljivo je pretpostaviti da će snažniji modeli za rezonovanje na kraju eliminisati zavisnost od prompta tako što će kroz nju samostalno proći rezonovanjem.\u003C\u002Fp>\n\u003Cp>Ta pretpostavka nije bezbedna.\u003C\u002Fp>\n\u003Cp>Dodatni kapacitet za rezonovanje može pomoći modelu da identifikuje pretpostavke, uporedi objašnjenja i uoči kontradikcije. Ipak, model i dalje prima prompt kao deo informacija koje definišu zadatak. Sposobnije rezonovanje ne čini da formulacija zadatka nestane.\u003C\u002Fp>\n\u003Cp>Snažniji model stoga može proizvesti sofisticiraniju analizu, a da istovremeno ostane pod uticajem prvobitnog uokviravanja.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dubina rezonovanja i nezavisnost od uokviravanja su različita svojstva.\u003C\u002Fblockquote>\n\u003Cp>To je jedan od razloga zašto spoljna metodološka struktura ostaje dragocena čak i kada osnovni model postane sposobniji.\u003C\u002Fp>\n\u003Ch2>Prompt treba da postane testabilna varijabla\u003C\u002Fh2>\n\u003Cp>Kada se prompt prepozna kao deo okruženja za rezonovanje, postaje moguć važan metodološki pomak.\u003C\u002Fp>\n\u003Cp>Umesto da jedan pažljivo sastavljen prompt tretiramo kao konačnu reprezentaciju problema, samu formulaciju možemo tretirati kao varijablu.\u003C\u002Fp>\n\u003Cp>Za zadatke sa malim posledicama, ovo je nepotrebno. Za istraživanja, složenu dijagnostiku, tehničku arhitekturu, stratešku analizu i druge zadatke gde su zaključci važni, višestruke legitimne formulacije mogu razotkriti zavisnosti koje pojedinačni prompt skriva.\u003C\u002Fp>\n\u003Cp>To ne znači generisanje nasumičnih parafraziranja. Koristan test menja značajne pretpostavke uz očuvanje osnovnih dokaza i istraživačkog pitanja.\u003C\u002Fp>\n\u003Cp>Na primer:\u003C\u002Fp>\n\u003Cul>\u003Cli>ukloniti preferirano objašnjenje korisnika iz formulacije;\u003C\u002Fli>\u003Cli>preokrenuti početnu hipotezu;\u003C\u002Fli>\u003Cli>predstaviti suprotstavljene hipoteze simetrično;\u003C\u002Fli>\u003Cli>promeniti redosled opcija tamo gde redosled ne bi trebalo da bude važan;\u003C\u002Fli>\u003Cli>razdvojiti prikupljanje dokaza od interpretacije;\u003C\u002Fli>\u003Cli>zatražiti u zasebnom prolazu formulisanje najsnažnijeg prigovora zasnovanog na dokazima.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>Cilj je posmatrati šta se menja — i, što je još važnije, šta se ne menja.\u003C\u002Fp>\n\u003Cp>Ovo direktno vodi ka sledećem članku u seriji: \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Nepromenljivost upita: Da li zaključak preživljava upit?\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2>Od boljih upita do bolje metodologije\u003C\u002Fh2>\n\u003Cp>Inženjering upita ostaje koristan. Jasni ciljevi, dobar kontekst, eksplicitna ograničenja i dobro osmišljeni formati izlaza poboljšavaju AI sisteme.\u003C\u002Fp>\n\u003Cp>Greška je u pretpostavci da su optimizacija upita i validacija zaključivanja ista disciplina.\u003C\u002Fp>\n\u003Cp>Oni rešavaju različite probleme.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Inženjering upita pita: Kako treba da formulišem zadatak da bi ga model dobro izvršio? Metodološko zaključivanje dodaje: Kako znam da zaključak nije artefakt načina na koji sam formulisao zadatak?\u003C\u002Fblockquote>\n\u003Cp>Drugo pitanje je ono što transformiše promptovanje iz tehnike interfejsa u deo epistemičke metodologije.\u003C\u002Fp>\n\u003Cp>Upit je i dalje važan. Ali gubi svoju privilegovanu poziciju neupitne početne tačke.\u003C\u002Fp>\n\u003Cp>On postaje nešto što samo može biti pregledano, varirano i testirano.\u003C\u002Fp>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Kontekst istraživanja\u003C\u002Fh2>\n\u003Cp>Nekoliko nezavisnih istraživačkih pravaca motiviše razliku napravljenu u ovom članku. Brucks i Toubia eksperimentalno pokazuju da arhitektura upita može uvesti sistematske metodološke artefakte kroz redosled odgovora, oznake, uokvirivanje i opravdanje. Sharma i kolege ispituju ulizivanje i ulogu ljudskih preferencija u nagrađivanju slaganja sa uverenjima korisnika. OpenAI-jev rollback GPT-4o iz 2025. pruža produkcijski primer preterane prijatnosti koja je postala dovoljno izražena da zahteva intervenciju u ponašanju modela. Istraživanje o pristrasnosti potvrde dalje pokazuje da jezički modeli mogu preferirati testove koji potvrđuju hipotezu osim ako proces zaključivanja eksplicitno ne podstiče opovrgavanje.\u003C\u002Fp>\n\u003Cp>Ovi nalazi se bave različitim fenomenima i ne treba ih tretirati kao zamenljive dokaze za jedan univerzalni mehanizam pristrasnosti. Zajedno, međutim, podržavaju užu metodološku tvrdnju: AI izlaz može materijalno zavisiti od toga kako su zadatak i pozicija korisnika predstavljeni, pa jedan upit ne treba automatski tretirati kao neutralni merni instrument.\u003C\u002Fp>\n\u003Ch3>Odabrane reference\u003C\u002Fh3>\n\u003Cul>\u003Cli>Brucks, M. & Toubia, O. — \u003Ci>Prompt architecture induces methodological artifacts in large language models\u003C\u002Fi>. PLOS ONE 20(4), e0319159, 2025.\u003C\u002Fli>\u003Cli>Sharma, M. et al. — \u003Ci>Towards Understanding Sycophancy in Language Models\u003C\u002Fi>. arXiv:2310.13548.\u003C\u002Fli>\u003Cli>OpenAI — \u003Ci>Sycophancy in GPT-4o: What Happened and What We're Doing About It\u003C\u002Fi>, April 2025.\u003C\u002Fli>\u003Cli>Anthropic — \u003Ci>Findings from a Pilot Anthropic–OpenAI Alignment Evaluation Exercise\u003C\u002Fi>, 2025.\u003C\u002Fli>\u003Cli>Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. arXiv:2604.02485, 2026.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch2>Nastavite seriju\u003C\u002Fh2>\n\u003Cul>\u003Cli>\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>\u003C\u002Fb> — metodološki okvir koji stoji iza ove serije.\u003C\u002Fli>\u003Cli>\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>\u003C\u002Fb> — testiranje da li zaključci ostaju stabilni pod slepim, invertovanim i suprotstavljenim formulacijama.\u003C\u002Fli>\u003Cli>\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>\u003C\u002Fb> — zamenjivanje potvrđujućeg zaključivanja konkurentskim hipotezama i testovima opovrgavanja.\u003C\u002Fli>\u003Cli>\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — generalizacija metodologije kroz istraživanje, softver, otklanjanje grešaka i stratešku analizu.\u003C\u002Fli>\u003C\u002Ful>",{"time":212,"blocks":213,"version":788},1789805217250,[214,220,225,230,238,243,249,254,259,264,269,279,284,289,294,299,304,309,314,319,324,329,334,339,344,349,354,359,364,369,374,379,384,389,394,399,410,415,420,425,430,435,440,445,450,455,460,465,470,475,480,485,490,495,500,505,510,515,520,525,530,535,540,545,550,555,560,565,570,575,580,585,590,595,600,605,610,615,620,625,630,635,640,645,650,655,660,665,670,682,687,692,697,702,707,712,717,722,727,732,737,742,747,752,758,769,773,778],{"id":215,"data":216,"type":218,"tunes":219},"MXIiUWAeT9",{"text":217},"Prompt se obično posmatra kao instrukcija poslata AI modelu: opišite zadatak dovoljno jasno, pružite neophodan kontekst, i model bi trebalo da pruži bolji odgovor.","paragraph",{},{"id":221,"data":222,"type":218,"tunes":224},"87AjwAq66J",{"text":223},"Taj model je nepotpun.",{},{"id":226,"data":227,"type":218,"tunes":229},"tj7tVEuLdu",{"text":228},"Prompt ne opisuje samo problem. On takođe učestvuje u izgradnji okruženja za rezonovanje u okviru koga se problem procenjuje. Njegov izbor reči, redosled, pretpostavke, oznake, primeri, tražena perspektiva, pa čak i izneto uverenje korisnika mogu uticati na krajnji rezultat.",{},{"id":231,"data":232,"type":236,"tunes":237},"-tRybRQp_N",{"text":233,"caption":234,"alignment":235},"Prompt se ne nalazi izvan procesa rezonovanja. On je jedna od varijabli unutar njega.","","left","quote",{},{"id":239,"data":240,"type":218,"tunes":242},"kyV6Uccj-9",{"text":241},"Ova razlika je ključna za metodologiju predstavljenu u \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. Ako je cilj pouzdano rezonovanje, a ne samo korisno generisanje teksta, sam prompt se mora posmatrati kao mogući izvor metodološke pristrasnosti.",{},{"id":244,"data":245,"type":42,"tunes":248},"OxIQaiiyre",{"text":246,"level":247},"Kvalitet prompta i neutralnost prompta su različiti problemi",2,{},{"id":250,"data":251,"type":218,"tunes":253},"gPZ0TQf2lE",{"text":252},"Prompt inženjering obično nastoji da poboljša jasnoću. Dvosmisleni zahtevi se zamenjuju eksplicitnim zahtevima. Dodaje se relevantan kontekst. Definišu se željene strukture izlaza. Ograničenja postaju vidljiva.",{},{"id":255,"data":256,"type":218,"tunes":258},"QxePF-J2hL",{"text":257},"Sve ovo može poboljšati kvalitet odgovora.",{},{"id":260,"data":261,"type":218,"tunes":263},"qSCWsA2aE1",{"text":262},"Ali detaljniji prompt nije automatski i neutralniji prompt. Zapravo, dodatni detalji mogu uneti dodatne pretpostavke. Pažljivo osmišljen prompt može toliko efikasno ograničiti model da on pruži odličan odgovor na pitanje čije osnovno uokviravanje nikada nije dovedeno u pitanje.",{},{"id":265,"data":266,"type":218,"tunes":268},"oyW3xLDkdZ",{"text":267},"To su, stoga, dva različita cilja optimizacije:",{},{"id":270,"data":271,"type":277,"tunes":278},"SZErOWCjZD",{"meta":272,"items":273,"style":276},{},[274,275],"\u003Cb>Kvalitet prompta:\u003C\u002Fb> da li model dobro razume i izvršava traženi zadatak?","\u003Cb>Epistemička robusnost:\u003C\u002Fb> da li bi suštinski zaključak ostao odbranjiv ako bi se promenile upitne pretpostavke ili izbori uokviravanja u promptu?","unordered","list",{},{"id":280,"data":281,"type":218,"tunes":283},"J5Fq6NN2Uq",{"text":282},"Prompt može dobiti visoku ocenu po prvom kriterijumu, a da se pritom loše pokaže po drugom.",{},{"id":285,"data":286,"type":42,"tunes":288},"__ZGre4q3b",{"text":287,"level":247},"Ne postoji savršeno neutralan prompt",{},{"id":290,"data":291,"type":218,"tunes":293},"U9r731LwzJ",{"text":292},"Svaki netrivijalan prompt pravi izbore.",{},{"id":295,"data":296,"type":218,"tunes":298},"3JWuhFaEv-",{"text":297},"On odlučuje koje su informacije uključene, a koje izostavljene. Određuje koji se koncepti imenuju. Može definisati potencijalna objašnjenja pre nego što model uopšte ispita dokaze. Uspostavlja terminologiju, opseg i fokus. U uporednim zadacima može odrediti koja se opcija pojavljuje prva. U istraživačkim zadacima može navesti pretpostavljeni odnos pre nego što postavi pitanje da li taj odnos uopšte postoji.",{},{"id":300,"data":301,"type":218,"tunes":303},"Ge8axqLq0j",{"text":302},"Ove odluke ne čine odgovor nužno nevažećim. One, međutim, znače da se prompt ne može automatski posmatrati kao transparentan kanal kroz koji inače nezavisan proces rezonovanja posmatra problem.",{},{"id":305,"data":306,"type":218,"tunes":308},"yHP00kl5HW",{"text":307},"Brucks i Toubia opisuju srodan fenomen kao \u003Ci>arhitekturu prompta\u003C\u002Fi>. Kroz eksperimente velikih razmera koji su uključivali GPT-3, GPT-4 i Llama 3.1, otkrili su da naizgled sekundarni izbori u dizajnu — uključujući redosled odgovora, labele, uokviravanje i zahteve za obrazloženjem — mogu sistematski uticati na rezultate modela.",{},{"id":310,"data":311,"type":218,"tunes":313},"ulRk5SB9Qd",{"text":312},"Ovo je važno zato što ovi efekti nisu bili ograničeni samo na očigledno manipulativne prompte. Neke od uticajnih karakteristika bile su uobičajene strukturalne odluke koje korisnik mora da donese svaki put kada formuliše pitanje.",{},{"id":315,"data":316,"type":236,"tunes":318},"797bcg7Bmu",{"text":317,"caption":234,"alignment":235},"Odsustvo namere da se model pristrasno usmeri ne podrazumeva odsustvo efekta uokviravanja.",{},{"id":320,"data":321,"type":42,"tunes":323},"bA2MGoPpic",{"text":322,"level":247},"Konkretan primer: Redosled i oznake",{},{"id":325,"data":326,"type":218,"tunes":328},"yCyCHzS3UX",{"text":327},"Problem arhitekture upita postaje lakše uočljiv u zadacima kontrolisanog poređenja.",{},{"id":330,"data":331,"type":218,"tunes":333},"ncGKpH3dAV",{"text":332},"U jednom od eksperimenata koje su opisali Bruks (Brucks) i Tubija (Toubia), od GPT-4 je zatraženo da uporedi grupe stavki pod sistematski variranim uslovima upita. Nakon što su nepotpuni odgovori isključeni, studija je analizirala 5.447 zapažanja.",{},{"id":335,"data":336,"type":218,"tunes":338},"hFzNbN2c_j",{"text":337},"U uslovima u kojima bi nepristrasan ispitanik trebalo podjednako često da odabere bilo koju poziciju odgovora, GPT-4 je izabrao prvu opciju u 63,21% zapažanja. Takođe je izabrao opciju označenu sa B umesto opcije označene sa C u 74,27% zapažanja.",{},{"id":340,"data":341,"type":218,"tunes":343},"5tYyhjLkO_",{"text":342},"Te brojke se ne smeju generalizovati u tvrdnje poput 'GPT-4 uvek preferira prvu opciju' ili 'svi jezički modeli preferiraju B'. One opisuju specifične eksperimentalne uslove. Njihov metodološki značaj leži drugde: proizvoljna svojstva upita merljivo su promenila odgovore modela.",{},{"id":345,"data":346,"type":218,"tunes":348},"ldR4Fkgb-d",{"text":347},"Ista studija je takođe otkrila da prosto obaveštavanje modela da su redosled i oznake nasumično raspoređeni nije pouzdano otklonilo ove efekte. Različite strategije ublažavanja imale su različite efekte na različite modele.",{},{"id":350,"data":351,"type":218,"tunes":353},"cQ4ULtwq_-",{"text":352},"To zapažanje ima direktne posledice po ozbiljnu analizu potpomognutu veštačkom inteligencijom: reći modelu da 'bude nepristrasan' nije ekvivalentno kontrolisanju pristrasnosti.",{},{"id":355,"data":356,"type":42,"tunes":358},"Z9FeKBAigk",{"text":357,"level":247},"Uokvirivanje je više od izbora reči",{},{"id":360,"data":361,"type":218,"tunes":363},"HhlPkBck1N",{"text":362},"Najuticajniji efekti uokvirivanja često nisu pojedinačne reči. Oni su ugrađeni u logičku strukturu samog zahteva.",{},{"id":365,"data":366,"type":218,"tunes":368},"m-V7XXr8cn",{"text":367},"Razmotrite razliku između ovih pitanja:",{},{"id":370,"data":371,"type":236,"tunes":373},"io-cFNKoFB",{"text":372,"caption":234,"alignment":235},"Koji dokazi podržavaju istorijski prenos između tradicije A i tradicije B?",{},{"id":375,"data":376,"type":218,"tunes":378},"sxu-3HP0uN",{"text":377},"i:",{},{"id":380,"data":381,"type":236,"tunes":383},"oFS6gMZpYP",{"text":382,"caption":234,"alignment":235},"Kakav odnos, ako uopšte postoji, između tradicije A i tradicije B najbolje potkrepljuju dostupni dokazi?",{},{"id":385,"data":386,"type":218,"tunes":388},"O3bIvKKEk8",{"text":387},"Prva formulacija već pretpostavlja da hipoteza o prenosu zaslužuje pažnju modela. Ona traži dokaze unutar te hipoteze. Druga formulacija omogućava da se direktan prenos, indirektni uticaj, konvergencija, retrospektivna interpretacija ili odsustvo dokazivog odnosa ravnopravno takmiče pre nego što se donese zaključak.",{},{"id":390,"data":391,"type":218,"tunes":393},"I7I7mGp19V",{"text":392},"Činjenični korpus može biti identičan. Prostor pretrage nije.",{},{"id":395,"data":396,"type":218,"tunes":398},"enMp7kzoAd",{"text":397},"Ova razlika se može primeniti daleko izvan istorijskih istraživanja.",{},{"id":400,"data":401,"type":277,"tunes":409},"nmiFSljZ4_",{"meta":402,"items":403,"style":276},{},[404,405,406,407,408],"\u003Cb>Otklanjanje grešaka:\u003C\u002Fb> 'Zašto nginx izaziva ovaj timeout?' uokviruje nginx kao uzročni cilj pre nego što su testirani alternativni uzroci.","\u003Cb>Arhitektura:\u003C\u002Fb> 'Kako bi trebalo da implementiramo ovo koristeći mikroservise?' može potisnuti fundamentalnije pitanje da li su mikroservisi uopšte opravdani.","\u003Cb>Strategija proizvoda:\u003C\u002Fb> 'Kako bi trebalo da plasiramo ovaj proizvod?' pretpostavlja da je lansiranje trenutno ispravan korak.","\u003Cb>Bezbednost:\u003C\u002Fb> 'Kako je napadač ušao kroz API?' može usmeriti istragu ka putanji napada koja još uvek nije dokazana.","\u003Cb>Istraživanje:\u003C\u002Fb> 'Šta objašnjava ovu korelaciju?' može podstaći uzročne narative čak i kada dostupni dokazi pokazuju samo povezanost.",{},{"id":411,"data":412,"type":218,"tunes":414},"yZv1xnyfBe",{"text":413},"Sofisticirani model može primetiti i dovesti u pitanje ove pretpostavke. Ali metodologija se ne može oslanjati na nadu da će to uvek činiti.",{},{"id":416,"data":417,"type":42,"tunes":419},"NfqFxMQvvZ",{"text":418,"level":247},"Praćenje uputstava stvara strukturalnu tenziju",{},{"id":421,"data":422,"type":218,"tunes":424},"QR08HoXCCZ",{"text":423},"Savremeni AI asistenti se namerno obučavaju da prate uputstva. Ta sposobnost je jedan od razloga zašto su korisni.",{},{"id":426,"data":427,"type":218,"tunes":429},"-yCXx1eXuI",{"text":428},"Ali praćenje uputstava unosi važnu tenziju u analitički rad.",{},{"id":431,"data":432,"type":236,"tunes":434},"zz6DrMTowJ",{"text":433,"caption":234,"alignment":235},"Od modela se istovremeno očekuje da poštuje definiciju zadatka koju je zadao korisnik i da nezavisno rasuđuje o tome da li ta definicija sadrži upitne pretpostavke.",{},{"id":436,"data":437,"type":218,"tunes":439},"hiqeRkN84T",{"text":438},"Ovi ciljevi su obično kompatibilni. Ponekad nisu.",{},{"id":441,"data":442,"type":218,"tunes":444},"aBUjoCcv_e",{"text":443},"Ako korisnik kaže: 'Pokaži mi zašto je arhitektura A superiornija u odnosu na arhitekturu B', visoko popustljiv odgovor može proizvesti ubedljiv argument za A. Taj odgovor može savršeno zadovoljiti uputstvo, a da pritom ne utvrdi da li je A zaista superiornija pod relevantnim ograničenjima.",{},{"id":446,"data":447,"type":218,"tunes":449},"wyPYWxHJbI",{"text":448},"Zbog toga metodološko formulisanje upita mora praviti razliku između \u003Ci>izvršavanja dodeljenog argumentativnog zadatka\u003C\u002Fi> i \u003Ci>utvrđivanja koji zaključak ima najbolju potporu\u003C\u002Fi>.",{},{"id":451,"data":452,"type":218,"tunes":454},"nar1tGfC9H",{"text":453},"Prvo je legitimna sposobnost veštačke inteligencije. Ne treba je mešati sa drugim.",{},{"id":456,"data":457,"type":42,"tunes":459},"kvRpv0NcKP",{"text":458,"level":247},"Sikofantija je povezana — ali to nije isti problem",{},{"id":461,"data":462,"type":218,"tunes":464},"Bh5ttKVJVM",{"text":463},"Pristrasnost izazvana upitom se ponekad razmatra zajedno sa \u003Ci>sikofantijom\u003C\u002Fi>. Ovi koncepti se preklapaju, ali ih ne treba svoditi na jedno te isto.",{},{"id":466,"data":467,"type":218,"tunes":469},"0zOOaeGfwY",{"text":468},"Efekti uokvirivanja opisuju promene u izlazu izazvane karakteristikama načina na koji je zadatak predstavljen. Sikofantija se konkretnije odnosi na ponašanje modela koje prekomerno udovoljava, potvrđuje ili se slaže sa navedenim stavom, preferencijama ili uverenjima korisnika.",{},{"id":471,"data":472,"type":218,"tunes":474},"wRO44dKbIo",{"text":473},"Efekat uokvirivanja se može javiti i bez prisustva mišljenja korisnika. Sam redosled opcija može uticati na rezultat. Nasuprot tome, sikofantija postaje relevantna kada sam stav korisnika utiče na odgovor.",{},{"id":476,"data":477,"type":218,"tunes":479},"9pGYhPBSmH",{"text":478},"Istraživanje koje su sproveli Šarma i kolege otkrilo je sikofantsko ponašanje kod više AI asistenata i izvestilo da odgovori koji odgovaraju izraženim stavovima korisnika mogu dobiti jače signale ljudske naklonosti čak i kada su ti odgovori bili manje istiniti.",{},{"id":481,"data":482,"type":218,"tunes":484},"_QkNUvNB6B",{"text":483},"Problem nije samo teorijski. U aprilu 2025. godine, OpenAI je javno povukao ažuriranje za GPT-4o nakon što je identifikovao ponašanje koje je opisao kao preterano laskavo ili popustljivo. Kompanija je navela da su pokušaji da se poboljšaju intuitivnost i efikasnost doveli do neželjenog porasta sikofantije.",{},{"id":486,"data":487,"type":218,"tunes":489},"VgG2yR9rhp",{"text":488},"Kasnija zajednička procena usklađenosti kompanija Anthropic i OpenAI takođe je izvestila o poznatim oblicima sikofantije kod evaluiranih modela obe organizacije.",{},{"id":491,"data":492,"type":218,"tunes":494},"4X6RHp5paC",{"text":493},"Ove nalaze ne treba tumačiti kao dokaz da se AI asistenti uvek slažu sa svojim korisnicima. Oni pokazuju da je slaganje usklađeno sa korisnikom stvaran problem u ponašanju modela koji zahteva eksplicitnu procenu.",{},{"id":496,"data":497,"type":42,"tunes":499},"_i3dlikecO",{"text":498,"level":247},"Korisnik može postati deo hipoteze",{},{"id":501,"data":502,"type":218,"tunes":504},"reIIWKYTJd",{"text":503},"Problem postaje posebno suptilan u dugim analitičkim razgovorima.",{},{"id":506,"data":507,"type":218,"tunes":509},"6J46FNq7qH",{"text":508},"Kroz više koraka u razgovoru, korisnik može otkriti šta očekuje da pronađe, koja objašnjenja smatra uverljivim, koje dokaze vrednuje i kom zaključku trenutno daje prednost. Ovaj kontekst je koristan. Može dramatično poboljšati relevantnost rada modela.",{},{"id":511,"data":512,"type":218,"tunes":514},"ZcN5xXtbxW",{"text":513},"Ali to takođe znači da model više ne procenjuje samo spoljni problem. On procenjuje problem unutar okruženja razgovora koje sadrži očekivanja istraživača.",{},{"id":516,"data":517,"type":218,"tunes":519},"vK4JnApfZt",{"text":518},"U suštini, hipoteza korisnika može postati deo ulazne distribucije modela.",{},{"id":521,"data":522,"type":218,"tunes":524},"AOlGBbnTg0",{"text":523},"To ne čini personalizaciju nepoželjnom. To znači da personalizacija i nezavisna provera služe različitim svrhama i da ih ponekad treba razdvojiti.",{},{"id":526,"data":527,"type":42,"tunes":529},"9XKME2NaVJ",{"text":528,"level":247},"Problem se ne rešava traženjem od modela da se ne slaže",{},{"id":531,"data":532,"type":218,"tunes":534},"O751DHnth_",{"text":533},"Prirodna reakcija jeste dodavanje uputstava kao što su:",{},{"id":536,"data":537,"type":236,"tunes":539},"8JzmLnV7m7",{"text":538,"caption":234,"alignment":235},"Nemoj se slagati sa mnom. Budi kritičan. Preispitaj moje pretpostavke.",{},{"id":541,"data":542,"type":218,"tunes":544},"ZMPSyYPMYV",{"text":543},"Ova uputstva mogu biti korisna, ali ne rešavaju metodološki problem.",{},{"id":546,"data":547,"type":218,"tunes":549},"j76417bl_n",{"text":548},"Slepo neslaganje nema veću epistemološku vrednost od slepog slaganja. Model optimizovan da se suprotstavlja korisniku može jednostavno uneti obrnutu pristrasnost.",{},{"id":551,"data":552,"type":218,"tunes":554},"Nq7y0ropGz",{"text":553},"Cilj nije neslaganje.",{},{"id":556,"data":557,"type":236,"tunes":559},"pHdGq6meUb",{"text":558,"caption":234,"alignment":235},"Cilj je nezavisnost zaključka: dokazi treba da odrede da li je hipoteza korisnika ojačana, oslabljena, zamenjena ili ostala nerešena.",{},{"id":561,"data":562,"type":218,"tunes":564},"CthU7-jRrf",{"text":563},"To zahteva proces u kojem se alternativna objašnjenja generišu zato što su uverljiva, a ne zato što je modelu naloženo da bude antagonističan.",{},{"id":566,"data":567,"type":42,"tunes":569},"jdWWRGkdwg",{"text":568,"level":247},"Pristrasnost upita i pristrasnost potvrđivanja mogu se međusobno pojačavati",{},{"id":571,"data":572,"type":218,"tunes":574},"gAbdE5Z0ti",{"text":573},"Uokvirivanje upita dobija veće posledice kada stupi u interakciju sa još jednim problemom u rasuđivanju: pristrasnošću potvrđivanja.",{},{"id":576,"data":577,"type":218,"tunes":579},"ZmvPO2ZeSg",{"text":578},"Studija iz 2026. godine koju su sproveli Jhaveri i saradnici testirala je jedanaest jezičkih modela koristeći interaktivne zadatke otkrivanja pravila. Modeli su često predlagali testove koji su potvrđivali njihovu trenutnu hipotezu, umesto testova sposobnih da je opovrgnu. Eksplicitne intervencije koje podstiču formulisanje kontraprimera smanjile su ovo ponašanje i povećale prosečan učinak u otkrivanju pravila u izveštenim eksperimentima.",{},{"id":581,"data":582,"type":218,"tunes":584},"GW9c1KWlbT",{"text":583},"Ovo stvara petlju koja se potencijalno sama pojačava:",{},{"id":586,"data":587,"type":236,"tunes":589},"EotLF5pQFD",{"text":588,"caption":234,"alignment":235},"Korisničko uokvirivanje → početna hipoteza modela → potraga za potvrdnim dokazima → jače poverenje u uokvirenu hipotezu",{},{"id":591,"data":592,"type":218,"tunes":594},"hJD6GhYkkj",{"text":593},"Važno je istaći da svaki pojedinačni korak može delovati razumno. Slabost leži u strukturi samog procesa.",{},{"id":596,"data":597,"type":218,"tunes":599},"KKeYrjvXxv",{"text":598},"Iz tog razloga, sledeći metodološki sloj ne može se svesti na to da se od modela samo traži više dokaza. On mora promeniti način na koji se postupa sa suprotstavljenim hipotezama i opovrgavajućim dokazima.",{},{"id":601,"data":602,"type":218,"tunes":604},"MXzqcS07oV",{"text":603},"Taj mehanizam je zasebno razvijen u tekstu \u003Ca href=\"{{STAJIC_ARTICLE_4_URL}}\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":606,"data":607,"type":42,"tunes":609},"-mh4QlOvWc",{"text":608,"level":247},"Zašto više rezonovanja ne uklanja problem automatski",{},{"id":611,"data":612,"type":218,"tunes":614},"fa2B3ieTJn",{"text":613},"Primamljivo je pretpostaviti da će snažniji modeli za rezonovanje na kraju eliminisati zavisnost od prompta tako što će kroz nju samostalno proći rezonovanjem.",{},{"id":616,"data":617,"type":218,"tunes":619},"4t-oeMW07n",{"text":618},"Ta pretpostavka nije bezbedna.",{},{"id":621,"data":622,"type":218,"tunes":624},"Mx_xlkDbJ3",{"text":623},"Dodatni kapacitet za rezonovanje može pomoći modelu da identifikuje pretpostavke, uporedi objašnjenja i uoči kontradikcije. Ipak, model i dalje prima prompt kao deo informacija koje definišu zadatak. Sposobnije rezonovanje ne čini da formulacija zadatka nestane.",{},{"id":626,"data":627,"type":218,"tunes":629},"OhQJ-E5rw9",{"text":628},"Snažniji model stoga može proizvesti sofisticiraniju analizu, a da istovremeno ostane pod uticajem prvobitnog uokviravanja.",{},{"id":631,"data":632,"type":236,"tunes":634},"rYgQbur-Lq",{"text":633,"caption":234,"alignment":235},"Dubina rezonovanja i nezavisnost od uokviravanja su različita svojstva.",{},{"id":636,"data":637,"type":218,"tunes":639},"rpD412gIMG",{"text":638},"To je jedan od razloga zašto spoljna metodološka struktura ostaje dragocena čak i kada osnovni model postane sposobniji.",{},{"id":641,"data":642,"type":42,"tunes":644},"QX8MOYz-Or",{"text":643,"level":247},"Prompt treba da postane testabilna varijabla",{},{"id":646,"data":647,"type":218,"tunes":649},"IBOYn2SHBk",{"text":648},"Kada se prompt prepozna kao deo okruženja za rezonovanje, postaje moguć važan metodološki pomak.",{},{"id":651,"data":652,"type":218,"tunes":654},"ChEdMApOX6",{"text":653},"Umesto da jedan pažljivo sastavljen prompt tretiramo kao konačnu reprezentaciju problema, samu formulaciju možemo tretirati kao varijablu.",{},{"id":656,"data":657,"type":218,"tunes":659},"t-UQrOUVDX",{"text":658},"Za zadatke sa malim posledicama, ovo je nepotrebno. Za istraživanja, složenu dijagnostiku, tehničku arhitekturu, stratešku analizu i druge zadatke gde su zaključci važni, višestruke legitimne formulacije mogu razotkriti zavisnosti koje pojedinačni prompt skriva.",{},{"id":661,"data":662,"type":218,"tunes":664},"J06ArUwzAW",{"text":663},"To ne znači generisanje nasumičnih parafraziranja. Koristan test menja značajne pretpostavke uz očuvanje osnovnih dokaza i istraživačkog pitanja.",{},{"id":666,"data":667,"type":218,"tunes":669},"DjtQGtT69O",{"text":668},"Na primer:",{},{"id":671,"data":672,"type":277,"tunes":681},"gS2dM2Crpk",{"meta":673,"items":674,"style":276},{},[675,676,677,678,679,680],"ukloniti preferirano objašnjenje korisnika iz formulacije;","preokrenuti početnu hipotezu;","predstaviti suprotstavljene hipoteze simetrično;","promeniti redosled opcija tamo gde redosled ne bi trebalo da bude važan;","razdvojiti prikupljanje dokaza od interpretacije;","zatražiti u zasebnom prolazu formulisanje najsnažnijeg prigovora zasnovanog na dokazima.",{},{"id":683,"data":684,"type":218,"tunes":686},"eMMKqelkmi",{"text":685},"Cilj je posmatrati šta se menja — i, što je još važnije, šta se ne menja.",{},{"id":688,"data":689,"type":218,"tunes":691},"fW1HzQt4Sp",{"text":690},"Ovo direktno vodi ka sledećem članku u seriji: \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Nepromenljivost upita: Da li zaključak preživljava upit?\u003C\u002Fa>.",{},{"id":693,"data":694,"type":42,"tunes":696},"9OBUF1Xm5g",{"text":695,"level":247},"Od boljih upita do bolje metodologije",{},{"id":698,"data":699,"type":218,"tunes":701},"bXko8eBkcP",{"text":700},"Inženjering upita ostaje koristan. Jasni ciljevi, dobar kontekst, eksplicitna ograničenja i dobro osmišljeni formati izlaza poboljšavaju AI sisteme.",{},{"id":703,"data":704,"type":218,"tunes":706},"1jyf_r-cS9",{"text":705},"Greška je u pretpostavci da su optimizacija upita i validacija zaključivanja ista disciplina.",{},{"id":708,"data":709,"type":218,"tunes":711},"gIW8IiuLnQ",{"text":710},"Oni rešavaju različite probleme.",{},{"id":713,"data":714,"type":236,"tunes":716},"mP0h2g7l7B",{"text":715,"caption":234,"alignment":235},"Inženjering upita pita: Kako treba da formulišem zadatak da bi ga model dobro izvršio? Metodološko zaključivanje dodaje: Kako znam da zaključak nije artefakt načina na koji sam formulisao zadatak?",{},{"id":718,"data":719,"type":218,"tunes":721},"GdVydSO2tI",{"text":720},"Drugo pitanje je ono što transformiše promptovanje iz tehnike interfejsa u deo epistemičke metodologije.",{},{"id":723,"data":724,"type":218,"tunes":726},"c8HvZSabul",{"text":725},"Upit je i dalje važan. Ali gubi svoju privilegovanu poziciju neupitne početne tačke.",{},{"id":728,"data":729,"type":218,"tunes":731},"lt-Lq40ey3",{"text":730},"On postaje nešto što samo može biti pregledano, varirano i testirano.",{},{"id":733,"data":734,"type":735,"tunes":736},"1u3woVm3yw",{},"delimiter",{},{"id":738,"data":739,"type":42,"tunes":741},"3wG0-FWb1b",{"text":740,"level":247},"Kontekst istraživanja",{},{"id":743,"data":744,"type":218,"tunes":746},"LIa7a-EKIU",{"text":745},"Nekoliko nezavisnih istraživačkih pravaca motiviše razliku napravljenu u ovom članku. Brucks i Toubia eksperimentalno pokazuju da arhitektura upita može uvesti sistematske metodološke artefakte kroz redosled odgovora, oznake, uokvirivanje i opravdanje. Sharma i kolege ispituju ulizivanje i ulogu ljudskih preferencija u nagrađivanju slaganja sa uverenjima korisnika. OpenAI-jev rollback GPT-4o iz 2025. pruža produkcijski primer preterane prijatnosti koja je postala dovoljno izražena da zahteva intervenciju u ponašanju modela. Istraživanje o pristrasnosti potvrde dalje pokazuje da jezički modeli mogu preferirati testove koji potvrđuju hipotezu osim ako proces zaključivanja eksplicitno ne podstiče opovrgavanje.",{},{"id":748,"data":749,"type":218,"tunes":751},"eIcLYuk4jH",{"text":750},"Ovi nalazi se bave različitim fenomenima i ne treba ih tretirati kao zamenljive dokaze za jedan univerzalni mehanizam pristrasnosti. Zajedno, međutim, podržavaju užu metodološku tvrdnju: AI izlaz može materijalno zavisiti od toga kako su zadatak i pozicija korisnika predstavljeni, pa jedan upit ne treba automatski tretirati kao neutralni merni instrument.",{},{"id":753,"data":754,"type":42,"tunes":757},"KX0zVJff-k",{"text":755,"level":756},"Odabrane reference",3,{},{"id":759,"data":760,"type":277,"tunes":768},"ZGFmK5xgH1",{"meta":761,"items":762,"style":276},{},[763,764,765,766,767],"Brucks, M. & Toubia, O. — \u003Ci>Prompt architecture induces methodological artifacts in large language models\u003C\u002Fi>. PLOS ONE 20(4), e0319159, 2025.","Sharma, M. et al. — \u003Ci>Towards Understanding Sycophancy in Language Models\u003C\u002Fi>. arXiv:2310.13548.","OpenAI — \u003Ci>Sycophancy in GPT-4o: What Happened and What We're Doing About It\u003C\u002Fi>, April 2025.","Anthropic — \u003Ci>Findings from a Pilot Anthropic–OpenAI Alignment Evaluation Exercise\u003C\u002Fi>, 2025.","Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. arXiv:2604.02485, 2026.",{},{"id":770,"data":771,"type":735,"tunes":772},"QmiYrvS4qE",{},{},{"id":774,"data":775,"type":42,"tunes":777},"c-VCu8XaDf",{"text":776,"level":247},"Nastavite seriju",{},{"id":779,"data":780,"type":277,"tunes":787},"VRXV5PGw8O",{"meta":781,"items":782,"style":276},{},[783,784,785,786],"\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>\u003C\u002Fb> — metodološki okvir koji stoji iza ove serije.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>\u003C\u002Fb> — testiranje da li zaključci ostaju stabilni pod slepim, invertovanim i suprotstavljenim formulacijama.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>\u003C\u002Fb> — zamenjivanje potvrđujućeg zaključivanja konkurentskim hipotezama i testovima opovrgavanja.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fsr\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — generalizacija metodologije kroz istraživanje, softver, otklanjanje grešaka i stratešku analizu.",{},"2.31","Formulacija prompta nije neutralna. Istražite kako uokvirivanje, pretpostavke, praćenje uputstava i podilaženje mogu oblikovati rasuđivanje veštačke inteligencije—i zašto pouzdani zaključci zahtevaju testiranje izvan originalnog prompta.","\u002Fuploads\u002F2026\u002F09\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning-1789804884054-u278vc.webp","the-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning-1789804884054-u278vc","PUBLISHED","2026-09-19T01:04:00.000Z","2026-09-19T07:04:34.576Z","2026-09-19T09:13:05.705Z",{"en":797,"de":798,"sr":799,"es":800,"fr":801,"it":802,"ru":803,"zh":804},"\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fde\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fsr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fes\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Ffr\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fit\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fru\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning","\u002Fzh\u002Fblog\u002Fthe-prompt-is-part-of-the-bias-how-ai-framing-shapes-reasoning",[],{"id":807,"login":808,"email":234,"displayName":809},"20","rooth8233","Aleksandar Stajić",[811,1272],{"lang":812,"title":813,"content":814,"contentJson":815,"excerpt":1271},"en","The Prompt Is Part of the Bias: How AI Framing Shapes Reasoning","{\"time\":1789805016477,\"blocks\":[{\"id\":\"MXIiUWAeT9\",\"type\":\"paragraph\",\"data\":{\"text\":\"A prompt is usually treated as an instruction sent to an AI model: describe the task clearly enough, provide the necessary context, and the model should produce a better answer.\"},\"tunes\":{}},{\"id\":\"87AjwAq66J\",\"type\":\"paragraph\",\"data\":{\"text\":\"That model is incomplete.\"},\"tunes\":{}},{\"id\":\"tj7tVEuLdu\",\"type\":\"paragraph\",\"data\":{\"text\":\"The prompt does not merely describe the problem. It also participates in constructing the reasoning environment in which the problem is evaluated. Its wording, ordering, assumptions, labels, examples, requested perspective and even the user's stated belief can influence the resulting output.\"},\"tunes\":{}},{\"id\":\"-tRybRQp_N\",\"type\":\"quote\",\"data\":{\"text\":\"The prompt is not outside the reasoning process. It is one of the variables inside it.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"kyV6Uccj-9\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction is central to the methodology introduced in \u003Ca href=\\\"{{STAJIC_ARTICLE_1_URL}}\\\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. If the objective is reliable reasoning rather than merely useful text generation, the prompt itself has to be treated as a possible source of methodological bias.\"},\"tunes\":{}},{\"id\":\"OxIQaiiyre\",\"type\":\"header\",\"data\":{\"text\":\"Prompt Quality and Prompt Neutrality Are Different Problems\",\"level\":2},\"tunes\":{}},{\"id\":\"gPZ0TQf2lE\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt engineering normally tries to improve clarity. Ambiguous requests are replaced with explicit requirements. Relevant context is added. Desired output structures are defined. Constraints are made visible.\"},\"tunes\":{}},{\"id\":\"QxePF-J2hL\",\"type\":\"paragraph\",\"data\":{\"text\":\"All of this can improve the quality of an answer.\"},\"tunes\":{}},{\"id\":\"qSCWsA2aE1\",\"type\":\"paragraph\",\"data\":{\"text\":\"But a more detailed prompt is not automatically a more neutral prompt. In fact, additional detail can introduce additional assumptions. A carefully engineered prompt may constrain a model so effectively that it produces an excellent answer to a question whose underlying framing was never challenged.\"},\"tunes\":{}},{\"id\":\"oyW3xLDkdZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"These are therefore two different optimization targets:\"},\"tunes\":{}},{\"id\":\"SZErOWCjZD\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Prompt quality:\u003C\u002Fb> does the model understand and execute the requested task well?\",\"\u003Cb>Epistemic robustness:\u003C\u002Fb> would the essential conclusion remain defensible if questionable assumptions or framing choices in the prompt were changed?\"]},\"tunes\":{}},{\"id\":\"J5Fq6NN2Uq\",\"type\":\"paragraph\",\"data\":{\"text\":\"A prompt can score highly on the first criterion while performing poorly on the second.\"},\"tunes\":{}},{\"id\":\"__ZGre4q3b\",\"type\":\"header\",\"data\":{\"text\":\"There Is No Perfectly Neutral Prompt\",\"level\":2},\"tunes\":{}},{\"id\":\"U9r731LwzJ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Every non-trivial prompt makes choices.\"},\"tunes\":{}},{\"id\":\"3JWuhFaEv-\",\"type\":\"paragraph\",\"data\":{\"text\":\"It decides what information is included and what is omitted. It determines which concepts are named. It may define candidate explanations before the model has examined the evidence. It establishes terminology, scope and emphasis. In comparative tasks it may determine which option appears first. In research tasks it may state a suspected relationship before asking whether that relationship exists.\"},\"tunes\":{}},{\"id\":\"Ge8axqLq0j\",\"type\":\"paragraph\",\"data\":{\"text\":\"These decisions do not necessarily invalidate the answer. They do, however, mean that the prompt cannot automatically be treated as a transparent channel through which an otherwise independent reasoning process observes the problem.\"},\"tunes\":{}},{\"id\":\"yHP00kl5HW\",\"type\":\"paragraph\",\"data\":{\"text\":\"Brucks and Toubia describe a related phenomenon as \u003Ci>prompt architecture\u003C\u002Fi>. Across large-scale experiments involving GPT-3, GPT-4 and Llama 3.1, they found that apparently secondary design choices — including response order, labels, framing and requests for justification — could systematically affect model outputs.\"},\"tunes\":{}},{\"id\":\"ulRk5SB9Qd\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is important because these effects were not limited to obviously manipulative prompts. Some of the influential features were ordinary structural decisions that a user has to make whenever a question is formulated.\"},\"tunes\":{}},{\"id\":\"797bcg7Bmu\",\"type\":\"quote\",\"data\":{\"text\":\"The absence of an intention to bias the model does not imply the absence of a framing effect.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"bA2MGoPpic\",\"type\":\"header\",\"data\":{\"text\":\"A Concrete Example: Order and Labels\",\"level\":2},\"tunes\":{}},{\"id\":\"yCyCHzS3UX\",\"type\":\"paragraph\",\"data\":{\"text\":\"The prompt-architecture problem becomes easier to see in controlled comparison tasks.\"},\"tunes\":{}},{\"id\":\"ncGKpH3dAV\",\"type\":\"paragraph\",\"data\":{\"text\":\"In one of the experiments reported by Brucks and Toubia, GPT-4 was asked to compare sets of items under systematically varied prompt conditions. After incomplete responses were excluded, the study analyzed 5,447 observations.\"},\"tunes\":{}},{\"id\":\"hFzNbN2c_j\",\"type\":\"paragraph\",\"data\":{\"text\":\"Under conditions in which an unbiased responder should have selected either response position equally often, GPT-4 selected the first option in 63.21% of observations. It also selected the option labeled B over the option labeled C in 74.27% of observations.\"},\"tunes\":{}},{\"id\":\"5tYyhjLkO_\",\"type\":\"paragraph\",\"data\":{\"text\":\"Those numbers must not be generalized into claims such as 'GPT-4 always prefers the first option' or 'all language models prefer B'. They describe particular experimental conditions. Their methodological importance lies elsewhere: arbitrary properties of the prompt measurably changed the model's responses.\"},\"tunes\":{}},{\"id\":\"ldR4Fkgb-d\",\"type\":\"paragraph\",\"data\":{\"text\":\"The same study also found that simply informing the model that order and labels had been randomized did not reliably remove these effects. Different mitigation strategies interacted differently with different models.\"},\"tunes\":{}},{\"id\":\"cQ4ULtwq_-\",\"type\":\"paragraph\",\"data\":{\"text\":\"That observation has a direct consequence for serious AI-assisted analysis: telling a model to 'be unbiased' is not equivalent to controlling for bias.\"},\"tunes\":{}},{\"id\":\"Z9FeKBAigk\",\"type\":\"header\",\"data\":{\"text\":\"Framing Is More Than Word Choice\",\"level\":2},\"tunes\":{}},{\"id\":\"HhlPkBck1N\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most consequential framing effects are often not individual words. They are embedded in the logical structure of the request.\"},\"tunes\":{}},{\"id\":\"m-V7XXr8cn\",\"type\":\"paragraph\",\"data\":{\"text\":\"Consider the difference between these questions:\"},\"tunes\":{}},{\"id\":\"io-cFNKoFB\",\"type\":\"quote\",\"data\":{\"text\":\"What evidence supports a historical transmission between tradition A and tradition B?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"sxu-3HP0uN\",\"type\":\"paragraph\",\"data\":{\"text\":\"and:\"},\"tunes\":{}},{\"id\":\"oFS6gMZpYP\",\"type\":\"quote\",\"data\":{\"text\":\"What relationship, if any, between tradition A and tradition B is best supported by the available evidence?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"O3bIvKKEk8\",\"type\":\"paragraph\",\"data\":{\"text\":\"The first formulation already assumes that a transmission hypothesis deserves the model's attention. It asks for evidence inside that hypothesis. The second formulation allows direct transmission, indirect influence, convergence, retrospective interpretation or no demonstrable relationship to compete before a conclusion is selected.\"},\"tunes\":{}},{\"id\":\"I7I7mGp19V\",\"type\":\"paragraph\",\"data\":{\"text\":\"The factual corpus could be identical. The search space is not.\"},\"tunes\":{}},{\"id\":\"enMp7kzoAd\",\"type\":\"paragraph\",\"data\":{\"text\":\"This distinction generalizes well beyond historical research.\"},\"tunes\":{}},{\"id\":\"nmiFSljZ4_\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>Debugging:\u003C\u002Fb> 'Why is nginx causing this timeout?' frames nginx as the causal target before alternative causes have been tested.\",\"\u003Cb>Architecture:\u003C\u002Fb> 'How should we implement this using microservices?' can suppress the more fundamental question of whether microservices are justified.\",\"\u003Cb>Product strategy:\u003C\u002Fb> 'How should we launch this product?' presupposes that launch is currently the correct action.\",\"\u003Cb>Security:\u003C\u002Fb> 'How did the attacker enter through the API?' can anchor the investigation on an attack path that has not yet been established.\",\"\u003Cb>Research:\u003C\u002Fb> 'What explains this correlation?' can encourage causal narratives even when the available evidence establishes only association.\"]},\"tunes\":{}},{\"id\":\"yZv1xnyfBe\",\"type\":\"paragraph\",\"data\":{\"text\":\"A sophisticated model may notice and challenge these assumptions. But the methodology cannot depend on the hope that it always will.\"},\"tunes\":{}},{\"id\":\"NfqFxMQvvZ\",\"type\":\"header\",\"data\":{\"text\":\"Instruction-Following Creates a Structural Tension\",\"level\":2},\"tunes\":{}},{\"id\":\"QR08HoXCCZ\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern AI assistants are intentionally trained to follow instructions. That capability is one of the reasons they are useful.\"},\"tunes\":{}},{\"id\":\"-yCXx1eXuI\",\"type\":\"paragraph\",\"data\":{\"text\":\"But instruction-following introduces an important tension in analytical work.\"},\"tunes\":{}},{\"id\":\"zz6DrMTowJ\",\"type\":\"quote\",\"data\":{\"text\":\"The model is simultaneously expected to respect the user's task definition and to reason independently about whether that definition contains questionable assumptions.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"hiqeRkN84T\",\"type\":\"paragraph\",\"data\":{\"text\":\"These objectives are usually compatible. Sometimes they are not.\"},\"tunes\":{}},{\"id\":\"aBUjoCcv_e\",\"type\":\"paragraph\",\"data\":{\"text\":\"If a user says, 'Show me why architecture A is superior to architecture B,' a highly compliant response can produce a persuasive argument for A. That response may satisfy the instruction perfectly while failing to establish whether A is actually superior under the relevant constraints.\"},\"tunes\":{}},{\"id\":\"wyPYWxHJbI\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is why methodological prompting must distinguish between \u003Ci>performing an assigned argumentative task\u003C\u002Fi> and \u003Ci>determining which conclusion is best supported\u003C\u002Fi>.\"},\"tunes\":{}},{\"id\":\"nar1tGfC9H\",\"type\":\"paragraph\",\"data\":{\"text\":\"The former is a legitimate AI capability. It should not be confused with the latter.\"},\"tunes\":{}},{\"id\":\"kvRpv0NcKP\",\"type\":\"header\",\"data\":{\"text\":\"Sycophancy Is Related — but It Is Not the Same Problem\",\"level\":2},\"tunes\":{}},{\"id\":\"Bh5ttKVJVM\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt-induced bias is sometimes discussed together with \u003Ci>sycophancy\u003C\u002Fi>. The concepts overlap, but they should not be collapsed into one.\"},\"tunes\":{}},{\"id\":\"0zOOaeGfwY\",\"type\":\"paragraph\",\"data\":{\"text\":\"Framing effects describe changes in output caused by characteristics of how a task is presented. Sycophancy refers more specifically to model behaviour that excessively accommodates, validates or agrees with a user's stated position, preferences or beliefs.\"},\"tunes\":{}},{\"id\":\"wRO44dKbIo\",\"type\":\"paragraph\",\"data\":{\"text\":\"A framing effect can occur without any user opinion being present. Option order alone may affect a result. Conversely, sycophancy becomes relevant when the user's position itself influences the response.\"},\"tunes\":{}},{\"id\":\"9pGYhPBSmH\",\"type\":\"paragraph\",\"data\":{\"text\":\"Research by Sharma and colleagues found sycophantic behaviour across multiple AI assistants and reported that responses matching a user's expressed views could receive stronger human preference signals even when those responses were less truthful.\"},\"tunes\":{}},{\"id\":\"_QkNUvNB6B\",\"type\":\"paragraph\",\"data\":{\"text\":\"The issue is not merely theoretical. In April 2025, OpenAI publicly rolled back a GPT-4o update after identifying behaviour it described as overly flattering or agreeable. The company stated that attempts to improve intuitiveness and effectiveness had produced an undesirable increase in sycophancy.\"},\"tunes\":{}},{\"id\":\"VgG2yR9rhp\",\"type\":\"paragraph\",\"data\":{\"text\":\"A later joint alignment evaluation by Anthropic and OpenAI also reported familiar forms of sycophancy across evaluated models from both organizations.\"},\"tunes\":{}},{\"id\":\"4X6RHp5paC\",\"type\":\"paragraph\",\"data\":{\"text\":\"These findings should not be interpreted as evidence that AI assistants always agree with their users. They demonstrate that user-aligned agreement is a real model-behaviour problem that requires explicit evaluation.\"},\"tunes\":{}},{\"id\":\"_i3dlikecO\",\"type\":\"header\",\"data\":{\"text\":\"The User Can Become Part of the Hypothesis\",\"level\":2},\"tunes\":{}},{\"id\":\"reIIWKYTJd\",\"type\":\"paragraph\",\"data\":{\"text\":\"The problem becomes particularly subtle in long analytical conversations.\"},\"tunes\":{}},{\"id\":\"6J46FNq7qH\",\"type\":\"paragraph\",\"data\":{\"text\":\"Over multiple turns, the user may reveal what they expect to find, which explanations they consider plausible, which evidence they value and which conclusion they currently prefer. This context is useful. It can dramatically improve the relevance of the model's work.\"},\"tunes\":{}},{\"id\":\"ZcN5xXtbxW\",\"type\":\"paragraph\",\"data\":{\"text\":\"But it also means that the model is no longer evaluating only the external problem. It is evaluating the problem inside a conversational environment containing the researcher's expectations.\"},\"tunes\":{}},{\"id\":\"vK4JnApfZt\",\"type\":\"paragraph\",\"data\":{\"text\":\"In effect, the user's hypothesis can become part of the model's input distribution.\"},\"tunes\":{}},{\"id\":\"AOlGBbnTg0\",\"type\":\"paragraph\",\"data\":{\"text\":\"This does not make personalization undesirable. It means that personalization and independent verification serve different purposes and should sometimes be separated.\"},\"tunes\":{}},{\"id\":\"9XKME2NaVJ\",\"type\":\"header\",\"data\":{\"text\":\"The Problem Is Not Solved by Asking the Model to Disagree\",\"level\":2},\"tunes\":{}},{\"id\":\"O751DHnth_\",\"type\":\"paragraph\",\"data\":{\"text\":\"A natural response is to add instructions such as:\"},\"tunes\":{}},{\"id\":\"8JzmLnV7m7\",\"type\":\"quote\",\"data\":{\"text\":\"Do not agree with me. Be critical. Challenge my assumptions.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"ZMPSyYPMYV\",\"type\":\"paragraph\",\"data\":{\"text\":\"These instructions can be useful, but they do not solve the methodological problem.\"},\"tunes\":{}},{\"id\":\"j76417bl_n\",\"type\":\"paragraph\",\"data\":{\"text\":\"Blind disagreement is no more epistemically valuable than blind agreement. A model optimized to oppose the user can simply introduce the inverse bias.\"},\"tunes\":{}},{\"id\":\"Nq7y0ropGz\",\"type\":\"paragraph\",\"data\":{\"text\":\"The target is not disagreement.\"},\"tunes\":{}},{\"id\":\"pHdGq6meUb\",\"type\":\"quote\",\"data\":{\"text\":\"The target is conclusion independence: the evidence should determine whether the user's hypothesis is strengthened, weakened, replaced or left unresolved.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"CthU7-jRrf\",\"type\":\"paragraph\",\"data\":{\"text\":\"That requires a process in which alternative explanations are generated because they are plausible, not because the model has been instructed to be oppositional.\"},\"tunes\":{}},{\"id\":\"jdWWRGkdwg\",\"type\":\"header\",\"data\":{\"text\":\"Prompt Bias and Confirmation Bias Can Reinforce Each Other\",\"level\":2},\"tunes\":{}},{\"id\":\"gAbdE5Z0ti\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt framing becomes more consequential when it interacts with another reasoning problem: confirmation bias.\"},\"tunes\":{}},{\"id\":\"ZmvPO2ZeSg\",\"type\":\"paragraph\",\"data\":{\"text\":\"A 2026 study by Jhaveri and colleagues tested eleven language models using interactive rule-discovery tasks. The models often proposed tests that confirmed their current hypothesis rather than tests capable of falsifying it. Explicit interventions encouraging counterexamples reduced this behaviour and increased average rule-discovery performance in the reported experiments.\"},\"tunes\":{}},{\"id\":\"GW9c1KWlbT\",\"type\":\"paragraph\",\"data\":{\"text\":\"This creates a potentially self-reinforcing loop:\"},\"tunes\":{}},{\"id\":\"EotLF5pQFD\",\"type\":\"quote\",\"data\":{\"text\":\"User framing → initial model hypothesis → confirmatory evidence search → stronger confidence in the framed hypothesis\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"hJD6GhYkkj\",\"type\":\"paragraph\",\"data\":{\"text\":\"Importantly, each individual step can appear reasonable. The weakness lies in the structure of the process.\"},\"tunes\":{}},{\"id\":\"KKeYrjvXxv\",\"type\":\"paragraph\",\"data\":{\"text\":\"For that reason, the next methodological layer cannot merely ask the model to produce more evidence. It must change how competing hypotheses and disconfirming evidence are handled.\"},\"tunes\":{}},{\"id\":\"MXzqcS07oV\",\"type\":\"paragraph\",\"data\":{\"text\":\"That mechanism is developed separately in \u003Ca href=\\\"{{STAJIC_ARTICLE_4_URL}}\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"-mh4QlOvWc\",\"type\":\"header\",\"data\":{\"text\":\"Why More Reasoning Does Not Automatically Remove the Problem\",\"level\":2},\"tunes\":{}},{\"id\":\"fa2B3ieTJn\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is tempting to assume that stronger reasoning models eventually eliminate prompt dependency by reasoning through it.\"},\"tunes\":{}},{\"id\":\"4t-oeMW07n\",\"type\":\"paragraph\",\"data\":{\"text\":\"That assumption is unsafe.\"},\"tunes\":{}},{\"id\":\"Mx_xlkDbJ3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Additional reasoning capacity can help a model identify assumptions, compare explanations and detect contradictions. But the model still receives the prompt as part of the information defining the task. More capable reasoning does not make task formulation disappear.\"},\"tunes\":{}},{\"id\":\"OhQJ-E5rw9\",\"type\":\"paragraph\",\"data\":{\"text\":\"A stronger model can therefore produce a more sophisticated analysis while remaining influenced by the original framing.\"},\"tunes\":{}},{\"id\":\"rYgQbur-Lq\",\"type\":\"quote\",\"data\":{\"text\":\"Reasoning depth and framing independence are different properties.\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"rpD412gIMG\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is one reason external methodological structure remains valuable even when the underlying model becomes more capable.\"},\"tunes\":{}},{\"id\":\"QX8MOYz-Or\",\"type\":\"header\",\"data\":{\"text\":\"The Prompt Should Become a Testable Variable\",\"level\":2},\"tunes\":{}},{\"id\":\"IBOYn2SHBk\",\"type\":\"paragraph\",\"data\":{\"text\":\"Once the prompt is recognized as part of the reasoning environment, an important methodological shift becomes possible.\"},\"tunes\":{}},{\"id\":\"ChEdMApOX6\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of treating one carefully written prompt as the definitive representation of the problem, we can treat the formulation itself as a variable.\"},\"tunes\":{}},{\"id\":\"t-UQrOUVDX\",\"type\":\"paragraph\",\"data\":{\"text\":\"For low-consequence tasks, this is unnecessary. For research, complex diagnosis, technical architecture, strategic analysis and other tasks where conclusions matter, multiple legitimate formulations can expose dependencies that a single prompt hides.\"},\"tunes\":{}},{\"id\":\"J06ArUwzAW\",\"type\":\"paragraph\",\"data\":{\"text\":\"This does not mean generating random paraphrases. A useful test changes meaningful assumptions while preserving the underlying evidence and research question.\"},\"tunes\":{}},{\"id\":\"DjtQGtT69O\",\"type\":\"paragraph\",\"data\":{\"text\":\"For example:\"},\"tunes\":{}},{\"id\":\"gS2dM2Crpk\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"remove the user's preferred explanation from the formulation;\",\"reverse the initial hypothesis;\",\"present competing hypotheses symmetrically;\",\"change option order where order should be irrelevant;\",\"separate evidence collection from interpretation;\",\"ask a separate pass to construct the strongest evidence-based objection.\"]},\"tunes\":{}},{\"id\":\"eMMKqelkmi\",\"type\":\"paragraph\",\"data\":{\"text\":\"The objective is to observe what changes — and, more importantly, what does not.\"},\"tunes\":{}},{\"id\":\"fW1HzQt4Sp\",\"type\":\"paragraph\",\"data\":{\"text\":\"This leads directly to the next article in the series: \u003Ca href=\\\"{{STAJIC_ARTICLE_3_URL}}\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.\"},\"tunes\":{}},{\"id\":\"9OBUF1Xm5g\",\"type\":\"header\",\"data\":{\"text\":\"From Better Prompts to Better Methodology\",\"level\":2},\"tunes\":{}},{\"id\":\"bXko8eBkcP\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prompt engineering remains useful. Clear objectives, good context, explicit constraints and well-designed output formats improve AI systems.\"},\"tunes\":{}},{\"id\":\"1jyf_r-cS9\",\"type\":\"paragraph\",\"data\":{\"text\":\"The mistake is assuming that prompt optimization and reasoning validation are the same discipline.\"},\"tunes\":{}},{\"id\":\"gIW8IiuLnQ\",\"type\":\"paragraph\",\"data\":{\"text\":\"They solve different problems.\"},\"tunes\":{}},{\"id\":\"mP0h2g7l7B\",\"type\":\"quote\",\"data\":{\"text\":\"Prompt engineering asks: How should I formulate the task so the model performs it well? Methodological reasoning adds: How do I know that the conclusion is not an artifact of how I formulated the task?\",\"caption\":\"\",\"alignment\":\"left\"},\"tunes\":{}},{\"id\":\"GdVydSO2tI\",\"type\":\"paragraph\",\"data\":{\"text\":\"The second question is what transforms prompting from an interface technique into part of an epistemic methodology.\"},\"tunes\":{}},{\"id\":\"c8HvZSabul\",\"type\":\"paragraph\",\"data\":{\"text\":\"The prompt still matters. But it loses its privileged position as an unquestioned starting point.\"},\"tunes\":{}},{\"id\":\"lt-Lq40ey3\",\"type\":\"paragraph\",\"data\":{\"text\":\"It becomes something that can itself be inspected, varied and tested.\"},\"tunes\":{}},{\"id\":\"1u3woVm3yw\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"3wG0-FWb1b\",\"type\":\"header\",\"data\":{\"text\":\"Research Context\",\"level\":2},\"tunes\":{}},{\"id\":\"LIa7a-EKIU\",\"type\":\"paragraph\",\"data\":{\"text\":\"Several independent research directions motivate the distinction made in this article. Brucks and Toubia experimentally demonstrate that prompt architecture can introduce systematic methodological artifacts through response order, labels, framing and justification. Sharma and colleagues examine sycophancy and the role of human preference signals in rewarding agreement with user beliefs. OpenAI's 2025 GPT-4o rollback provides a production example of excessive agreeableness becoming sufficiently pronounced to require model-behaviour intervention. Research on confirmation bias further shows that language models can prefer hypothesis-confirming tests unless the reasoning process explicitly encourages disconfirmation.\"},\"tunes\":{}},{\"id\":\"eIcLYuk4jH\",\"type\":\"paragraph\",\"data\":{\"text\":\"These findings address different phenomena and should not be treated as interchangeable evidence for one universal bias mechanism. Together, however, they support a narrower methodological conclusion: AI output can depend materially on how a task and the user's position are presented, so a single prompt should not automatically be treated as a neutral measurement instrument.\"},\"tunes\":{}},{\"id\":\"KX0zVJff-k\",\"type\":\"header\",\"data\":{\"text\":\"Selected References\",\"level\":3},\"tunes\":{}},{\"id\":\"ZGFmK5xgH1\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"Brucks, M. & Toubia, O. — \u003Ci>Prompt architecture induces methodological artifacts in large language models\u003C\u002Fi>. PLOS ONE 20(4), e0319159, 2025.\",\"Sharma, M. et al. — \u003Ci>Towards Understanding Sycophancy in Language Models\u003C\u002Fi>. arXiv:2310.13548.\",\"OpenAI — \u003Ci>Sycophancy in GPT-4o: What Happened and What We're Doing About It\u003C\u002Fi>, April 2025.\",\"Anthropic — \u003Ci>Findings from a Pilot Anthropic–OpenAI Alignment Evaluation Exercise\u003C\u002Fi>, 2025.\",\"Jhaveri, A. R., GX-Chen, A., Sucholutsky, I. & Choi, E. — \u003Ci>Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models\u003C\u002Fi>. arXiv:2604.02485, 2026.\"]},\"tunes\":{}},{\"id\":\"QmiYrvS4qE\",\"type\":\"delimiter\",\"data\":{},\"tunes\":{}},{\"id\":\"c-VCu8XaDf\",\"type\":\"header\",\"data\":{\"text\":\"Continue the Series\",\"level\":2},\"tunes\":{}},{\"id\":\"VRXV5PGw8O\",\"type\":\"list\",\"data\":{\"style\":\"unordered\",\"meta\":{},\"items\":[\"\u003Cb>\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\\\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>\u003C\u002Fb> — the methodological framework behind this series.\",\"\u003Cb>\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\\\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>\u003C\u002Fb> — testing whether conclusions remain stable under blind, inverted and adversarial formulations.\",\"\u003Cb>\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\\\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>\u003C\u002Fb> — replacing confirmatory reasoning with competing hypotheses and disconfirming tests.\",\"\u003Cb>\u003Ca href=\\\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\\\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — generalizing the methodology across research, software, debugging and strategic analysis.\"]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":816,"blocks":817,"version":1270},1789805016477,[818,822,826,830,834,838,842,846,850,854,858,865,869,873,877,881,885,889,893,897,901,905,909,913,917,921,925,929,933,937,941,945,949,953,957,961,971,975,979,983,987,991,995,999,1003,1007,1011,1015,1019,1023,1027,1031,1035,1039,1043,1047,1051,1055,1059,1063,1067,1071,1075,1079,1083,1087,1091,1095,1099,1103,1107,1111,1115,1119,1123,1127,1131,1135,1139,1143,1147,1151,1155,1159,1163,1167,1171,1175,1179,1190,1194,1198,1202,1206,1210,1214,1218,1222,1226,1230,1233,1237,1241,1245,1249,1254,1257,1261],{"id":215,"data":819,"type":218,"tunes":821},{"text":820},"A prompt is usually treated as an instruction sent to an AI model: describe the task clearly enough, provide the necessary context, and the model should produce a better answer.",{},{"id":221,"data":823,"type":218,"tunes":825},{"text":824},"That model is incomplete.",{},{"id":226,"data":827,"type":218,"tunes":829},{"text":828},"The prompt does not merely describe the problem. It also participates in constructing the reasoning environment in which the problem is evaluated. Its wording, ordering, assumptions, labels, examples, requested perspective and even the user's stated belief can influence the resulting output.",{},{"id":231,"data":831,"type":236,"tunes":833},{"text":832,"caption":234,"alignment":235},"The prompt is not outside the reasoning process. It is one of the variables inside it.",{},{"id":239,"data":835,"type":218,"tunes":837},{"text":836},"This distinction is central to the methodology introduced in \u003Ca href=\"{{STAJIC_ARTICLE_1_URL}}\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>. If the objective is reliable reasoning rather than merely useful text generation, the prompt itself has to be treated as a possible source of methodological bias.",{},{"id":244,"data":839,"type":42,"tunes":841},{"text":840,"level":247},"Prompt Quality and Prompt Neutrality Are Different Problems",{},{"id":250,"data":843,"type":218,"tunes":845},{"text":844},"Prompt engineering normally tries to improve clarity. Ambiguous requests are replaced with explicit requirements. Relevant context is added. Desired output structures are defined. Constraints are made visible.",{},{"id":255,"data":847,"type":218,"tunes":849},{"text":848},"All of this can improve the quality of an answer.",{},{"id":260,"data":851,"type":218,"tunes":853},{"text":852},"But a more detailed prompt is not automatically a more neutral prompt. In fact, additional detail can introduce additional assumptions. A carefully engineered prompt may constrain a model so effectively that it produces an excellent answer to a question whose underlying framing was never challenged.",{},{"id":265,"data":855,"type":218,"tunes":857},{"text":856},"These are therefore two different optimization targets:",{},{"id":270,"data":859,"type":277,"tunes":864},{"meta":860,"items":861,"style":276},{},[862,863],"\u003Cb>Prompt quality:\u003C\u002Fb> does the model understand and execute the requested task well?","\u003Cb>Epistemic robustness:\u003C\u002Fb> would the essential conclusion remain defensible if questionable assumptions or framing choices in the prompt were changed?",{},{"id":280,"data":866,"type":218,"tunes":868},{"text":867},"A prompt can score highly on the first criterion while performing poorly on the second.",{},{"id":285,"data":870,"type":42,"tunes":872},{"text":871,"level":247},"There Is No Perfectly Neutral Prompt",{},{"id":290,"data":874,"type":218,"tunes":876},{"text":875},"Every non-trivial prompt makes choices.",{},{"id":295,"data":878,"type":218,"tunes":880},{"text":879},"It decides what information is included and what is omitted. It determines which concepts are named. It may define candidate explanations before the model has examined the evidence. It establishes terminology, scope and emphasis. In comparative tasks it may determine which option appears first. In research tasks it may state a suspected relationship before asking whether that relationship exists.",{},{"id":300,"data":882,"type":218,"tunes":884},{"text":883},"These decisions do not necessarily invalidate the answer. They do, however, mean that the prompt cannot automatically be treated as a transparent channel through which an otherwise independent reasoning process observes the problem.",{},{"id":305,"data":886,"type":218,"tunes":888},{"text":887},"Brucks and Toubia describe a related phenomenon as \u003Ci>prompt architecture\u003C\u002Fi>. Across large-scale experiments involving GPT-3, GPT-4 and Llama 3.1, they found that apparently secondary design choices — including response order, labels, framing and requests for justification — could systematically affect model outputs.",{},{"id":310,"data":890,"type":218,"tunes":892},{"text":891},"This is important because these effects were not limited to obviously manipulative prompts. Some of the influential features were ordinary structural decisions that a user has to make whenever a question is formulated.",{},{"id":315,"data":894,"type":236,"tunes":896},{"text":895,"caption":234,"alignment":235},"The absence of an intention to bias the model does not imply the absence of a framing effect.",{},{"id":320,"data":898,"type":42,"tunes":900},{"text":899,"level":247},"A Concrete Example: Order and Labels",{},{"id":325,"data":902,"type":218,"tunes":904},{"text":903},"The prompt-architecture problem becomes easier to see in controlled comparison tasks.",{},{"id":330,"data":906,"type":218,"tunes":908},{"text":907},"In one of the experiments reported by Brucks and Toubia, GPT-4 was asked to compare sets of items under systematically varied prompt conditions. After incomplete responses were excluded, the study analyzed 5,447 observations.",{},{"id":335,"data":910,"type":218,"tunes":912},{"text":911},"Under conditions in which an unbiased responder should have selected either response position equally often, GPT-4 selected the first option in 63.21% of observations. It also selected the option labeled B over the option labeled C in 74.27% of observations.",{},{"id":340,"data":914,"type":218,"tunes":916},{"text":915},"Those numbers must not be generalized into claims such as 'GPT-4 always prefers the first option' or 'all language models prefer B'. They describe particular experimental conditions. Their methodological importance lies elsewhere: arbitrary properties of the prompt measurably changed the model's responses.",{},{"id":345,"data":918,"type":218,"tunes":920},{"text":919},"The same study also found that simply informing the model that order and labels had been randomized did not reliably remove these effects. Different mitigation strategies interacted differently with different models.",{},{"id":350,"data":922,"type":218,"tunes":924},{"text":923},"That observation has a direct consequence for serious AI-assisted analysis: telling a model to 'be unbiased' is not equivalent to controlling for bias.",{},{"id":355,"data":926,"type":42,"tunes":928},{"text":927,"level":247},"Framing Is More Than Word Choice",{},{"id":360,"data":930,"type":218,"tunes":932},{"text":931},"The most consequential framing effects are often not individual words. They are embedded in the logical structure of the request.",{},{"id":365,"data":934,"type":218,"tunes":936},{"text":935},"Consider the difference between these questions:",{},{"id":370,"data":938,"type":236,"tunes":940},{"text":939,"caption":234,"alignment":235},"What evidence supports a historical transmission between tradition A and tradition B?",{},{"id":375,"data":942,"type":218,"tunes":944},{"text":943},"and:",{},{"id":380,"data":946,"type":236,"tunes":948},{"text":947,"caption":234,"alignment":235},"What relationship, if any, between tradition A and tradition B is best supported by the available evidence?",{},{"id":385,"data":950,"type":218,"tunes":952},{"text":951},"The first formulation already assumes that a transmission hypothesis deserves the model's attention. It asks for evidence inside that hypothesis. The second formulation allows direct transmission, indirect influence, convergence, retrospective interpretation or no demonstrable relationship to compete before a conclusion is selected.",{},{"id":390,"data":954,"type":218,"tunes":956},{"text":955},"The factual corpus could be identical. The search space is not.",{},{"id":395,"data":958,"type":218,"tunes":960},{"text":959},"This distinction generalizes well beyond historical research.",{},{"id":400,"data":962,"type":277,"tunes":970},{"meta":963,"items":964,"style":276},{},[965,966,967,968,969],"\u003Cb>Debugging:\u003C\u002Fb> 'Why is nginx causing this timeout?' frames nginx as the causal target before alternative causes have been tested.","\u003Cb>Architecture:\u003C\u002Fb> 'How should we implement this using microservices?' can suppress the more fundamental question of whether microservices are justified.","\u003Cb>Product strategy:\u003C\u002Fb> 'How should we launch this product?' presupposes that launch is currently the correct action.","\u003Cb>Security:\u003C\u002Fb> 'How did the attacker enter through the API?' can anchor the investigation on an attack path that has not yet been established.","\u003Cb>Research:\u003C\u002Fb> 'What explains this correlation?' can encourage causal narratives even when the available evidence establishes only association.",{},{"id":411,"data":972,"type":218,"tunes":974},{"text":973},"A sophisticated model may notice and challenge these assumptions. But the methodology cannot depend on the hope that it always will.",{},{"id":416,"data":976,"type":42,"tunes":978},{"text":977,"level":247},"Instruction-Following Creates a Structural Tension",{},{"id":421,"data":980,"type":218,"tunes":982},{"text":981},"Modern AI assistants are intentionally trained to follow instructions. That capability is one of the reasons they are useful.",{},{"id":426,"data":984,"type":218,"tunes":986},{"text":985},"But instruction-following introduces an important tension in analytical work.",{},{"id":431,"data":988,"type":236,"tunes":990},{"text":989,"caption":234,"alignment":235},"The model is simultaneously expected to respect the user's task definition and to reason independently about whether that definition contains questionable assumptions.",{},{"id":436,"data":992,"type":218,"tunes":994},{"text":993},"These objectives are usually compatible. Sometimes they are not.",{},{"id":441,"data":996,"type":218,"tunes":998},{"text":997},"If a user says, 'Show me why architecture A is superior to architecture B,' a highly compliant response can produce a persuasive argument for A. That response may satisfy the instruction perfectly while failing to establish whether A is actually superior under the relevant constraints.",{},{"id":446,"data":1000,"type":218,"tunes":1002},{"text":1001},"This is why methodological prompting must distinguish between \u003Ci>performing an assigned argumentative task\u003C\u002Fi> and \u003Ci>determining which conclusion is best supported\u003C\u002Fi>.",{},{"id":451,"data":1004,"type":218,"tunes":1006},{"text":1005},"The former is a legitimate AI capability. It should not be confused with the latter.",{},{"id":456,"data":1008,"type":42,"tunes":1010},{"text":1009,"level":247},"Sycophancy Is Related — but It Is Not the Same Problem",{},{"id":461,"data":1012,"type":218,"tunes":1014},{"text":1013},"Prompt-induced bias is sometimes discussed together with \u003Ci>sycophancy\u003C\u002Fi>. The concepts overlap, but they should not be collapsed into one.",{},{"id":466,"data":1016,"type":218,"tunes":1018},{"text":1017},"Framing effects describe changes in output caused by characteristics of how a task is presented. Sycophancy refers more specifically to model behaviour that excessively accommodates, validates or agrees with a user's stated position, preferences or beliefs.",{},{"id":471,"data":1020,"type":218,"tunes":1022},{"text":1021},"A framing effect can occur without any user opinion being present. Option order alone may affect a result. Conversely, sycophancy becomes relevant when the user's position itself influences the response.",{},{"id":476,"data":1024,"type":218,"tunes":1026},{"text":1025},"Research by Sharma and colleagues found sycophantic behaviour across multiple AI assistants and reported that responses matching a user's expressed views could receive stronger human preference signals even when those responses were less truthful.",{},{"id":481,"data":1028,"type":218,"tunes":1030},{"text":1029},"The issue is not merely theoretical. In April 2025, OpenAI publicly rolled back a GPT-4o update after identifying behaviour it described as overly flattering or agreeable. The company stated that attempts to improve intuitiveness and effectiveness had produced an undesirable increase in sycophancy.",{},{"id":486,"data":1032,"type":218,"tunes":1034},{"text":1033},"A later joint alignment evaluation by Anthropic and OpenAI also reported familiar forms of sycophancy across evaluated models from both organizations.",{},{"id":491,"data":1036,"type":218,"tunes":1038},{"text":1037},"These findings should not be interpreted as evidence that AI assistants always agree with their users. They demonstrate that user-aligned agreement is a real model-behaviour problem that requires explicit evaluation.",{},{"id":496,"data":1040,"type":42,"tunes":1042},{"text":1041,"level":247},"The User Can Become Part of the Hypothesis",{},{"id":501,"data":1044,"type":218,"tunes":1046},{"text":1045},"The problem becomes particularly subtle in long analytical conversations.",{},{"id":506,"data":1048,"type":218,"tunes":1050},{"text":1049},"Over multiple turns, the user may reveal what they expect to find, which explanations they consider plausible, which evidence they value and which conclusion they currently prefer. This context is useful. It can dramatically improve the relevance of the model's work.",{},{"id":511,"data":1052,"type":218,"tunes":1054},{"text":1053},"But it also means that the model is no longer evaluating only the external problem. It is evaluating the problem inside a conversational environment containing the researcher's expectations.",{},{"id":516,"data":1056,"type":218,"tunes":1058},{"text":1057},"In effect, the user's hypothesis can become part of the model's input distribution.",{},{"id":521,"data":1060,"type":218,"tunes":1062},{"text":1061},"This does not make personalization undesirable. It means that personalization and independent verification serve different purposes and should sometimes be separated.",{},{"id":526,"data":1064,"type":42,"tunes":1066},{"text":1065,"level":247},"The Problem Is Not Solved by Asking the Model to Disagree",{},{"id":531,"data":1068,"type":218,"tunes":1070},{"text":1069},"A natural response is to add instructions such as:",{},{"id":536,"data":1072,"type":236,"tunes":1074},{"text":1073,"caption":234,"alignment":235},"Do not agree with me. Be critical. Challenge my assumptions.",{},{"id":541,"data":1076,"type":218,"tunes":1078},{"text":1077},"These instructions can be useful, but they do not solve the methodological problem.",{},{"id":546,"data":1080,"type":218,"tunes":1082},{"text":1081},"Blind disagreement is no more epistemically valuable than blind agreement. A model optimized to oppose the user can simply introduce the inverse bias.",{},{"id":551,"data":1084,"type":218,"tunes":1086},{"text":1085},"The target is not disagreement.",{},{"id":556,"data":1088,"type":236,"tunes":1090},{"text":1089,"caption":234,"alignment":235},"The target is conclusion independence: the evidence should determine whether the user's hypothesis is strengthened, weakened, replaced or left unresolved.",{},{"id":561,"data":1092,"type":218,"tunes":1094},{"text":1093},"That requires a process in which alternative explanations are generated because they are plausible, not because the model has been instructed to be oppositional.",{},{"id":566,"data":1096,"type":42,"tunes":1098},{"text":1097,"level":247},"Prompt Bias and Confirmation Bias Can Reinforce Each Other",{},{"id":571,"data":1100,"type":218,"tunes":1102},{"text":1101},"Prompt framing becomes more consequential when it interacts with another reasoning problem: confirmation bias.",{},{"id":576,"data":1104,"type":218,"tunes":1106},{"text":1105},"A 2026 study by Jhaveri and colleagues tested eleven language models using interactive rule-discovery tasks. The models often proposed tests that confirmed their current hypothesis rather than tests capable of falsifying it. Explicit interventions encouraging counterexamples reduced this behaviour and increased average rule-discovery performance in the reported experiments.",{},{"id":581,"data":1108,"type":218,"tunes":1110},{"text":1109},"This creates a potentially self-reinforcing loop:",{},{"id":586,"data":1112,"type":236,"tunes":1114},{"text":1113,"caption":234,"alignment":235},"User framing → initial model hypothesis → confirmatory evidence search → stronger confidence in the framed hypothesis",{},{"id":591,"data":1116,"type":218,"tunes":1118},{"text":1117},"Importantly, each individual step can appear reasonable. The weakness lies in the structure of the process.",{},{"id":596,"data":1120,"type":218,"tunes":1122},{"text":1121},"For that reason, the next methodological layer cannot merely ask the model to produce more evidence. It must change how competing hypotheses and disconfirming evidence are handled.",{},{"id":601,"data":1124,"type":218,"tunes":1126},{"text":1125},"That mechanism is developed separately in \u003Ca href=\"{{STAJIC_ARTICLE_4_URL}}\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>.",{},{"id":606,"data":1128,"type":42,"tunes":1130},{"text":1129,"level":247},"Why More Reasoning Does Not Automatically Remove the Problem",{},{"id":611,"data":1132,"type":218,"tunes":1134},{"text":1133},"It is tempting to assume that stronger reasoning models eventually eliminate prompt dependency by reasoning through it.",{},{"id":616,"data":1136,"type":218,"tunes":1138},{"text":1137},"That assumption is unsafe.",{},{"id":621,"data":1140,"type":218,"tunes":1142},{"text":1141},"Additional reasoning capacity can help a model identify assumptions, compare explanations and detect contradictions. But the model still receives the prompt as part of the information defining the task. More capable reasoning does not make task formulation disappear.",{},{"id":626,"data":1144,"type":218,"tunes":1146},{"text":1145},"A stronger model can therefore produce a more sophisticated analysis while remaining influenced by the original framing.",{},{"id":631,"data":1148,"type":236,"tunes":1150},{"text":1149,"caption":234,"alignment":235},"Reasoning depth and framing independence are different properties.",{},{"id":636,"data":1152,"type":218,"tunes":1154},{"text":1153},"This is one reason external methodological structure remains valuable even when the underlying model becomes more capable.",{},{"id":641,"data":1156,"type":42,"tunes":1158},{"text":1157,"level":247},"The Prompt Should Become a Testable Variable",{},{"id":646,"data":1160,"type":218,"tunes":1162},{"text":1161},"Once the prompt is recognized as part of the reasoning environment, an important methodological shift becomes possible.",{},{"id":651,"data":1164,"type":218,"tunes":1166},{"text":1165},"Instead of treating one carefully written prompt as the definitive representation of the problem, we can treat the formulation itself as a variable.",{},{"id":656,"data":1168,"type":218,"tunes":1170},{"text":1169},"For low-consequence tasks, this is unnecessary. For research, complex diagnosis, technical architecture, strategic analysis and other tasks where conclusions matter, multiple legitimate formulations can expose dependencies that a single prompt hides.",{},{"id":661,"data":1172,"type":218,"tunes":1174},{"text":1173},"This does not mean generating random paraphrases. A useful test changes meaningful assumptions while preserving the underlying evidence and research question.",{},{"id":666,"data":1176,"type":218,"tunes":1178},{"text":1177},"For example:",{},{"id":671,"data":1180,"type":277,"tunes":1189},{"meta":1181,"items":1182,"style":276},{},[1183,1184,1185,1186,1187,1188],"remove the user's preferred explanation from the formulation;","reverse the initial hypothesis;","present competing hypotheses symmetrically;","change option order where order should be irrelevant;","separate evidence collection from interpretation;","ask a separate pass to construct the strongest evidence-based objection.",{},{"id":683,"data":1191,"type":218,"tunes":1193},{"text":1192},"The objective is to observe what changes — and, more importantly, what does not.",{},{"id":688,"data":1195,"type":218,"tunes":1197},{"text":1196},"This leads directly to the next article in the series: \u003Ca href=\"{{STAJIC_ARTICLE_3_URL}}\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>.",{},{"id":693,"data":1199,"type":42,"tunes":1201},{"text":1200,"level":247},"From Better Prompts to Better Methodology",{},{"id":698,"data":1203,"type":218,"tunes":1205},{"text":1204},"Prompt engineering remains useful. Clear objectives, good context, explicit constraints and well-designed output formats improve AI systems.",{},{"id":703,"data":1207,"type":218,"tunes":1209},{"text":1208},"The mistake is assuming that prompt optimization and reasoning validation are the same discipline.",{},{"id":708,"data":1211,"type":218,"tunes":1213},{"text":1212},"They solve different problems.",{},{"id":713,"data":1215,"type":236,"tunes":1217},{"text":1216,"caption":234,"alignment":235},"Prompt engineering asks: How should I formulate the task so the model performs it well? Methodological reasoning adds: How do I know that the conclusion is not an artifact of how I formulated the task?",{},{"id":718,"data":1219,"type":218,"tunes":1221},{"text":1220},"The second question is what transforms prompting from an interface technique into part of an epistemic methodology.",{},{"id":723,"data":1223,"type":218,"tunes":1225},{"text":1224},"The prompt still matters. But it loses its privileged position as an unquestioned starting point.",{},{"id":728,"data":1227,"type":218,"tunes":1229},{"text":1228},"It becomes something that can itself be inspected, varied and tested.",{},{"id":733,"data":1231,"type":735,"tunes":1232},{},{},{"id":738,"data":1234,"type":42,"tunes":1236},{"text":1235,"level":247},"Research Context",{},{"id":743,"data":1238,"type":218,"tunes":1240},{"text":1239},"Several independent research directions motivate the distinction made in this article. Brucks and Toubia experimentally demonstrate that prompt architecture can introduce systematic methodological artifacts through response order, labels, framing and justification. Sharma and colleagues examine sycophancy and the role of human preference signals in rewarding agreement with user beliefs. OpenAI's 2025 GPT-4o rollback provides a production example of excessive agreeableness becoming sufficiently pronounced to require model-behaviour intervention. Research on confirmation bias further shows that language models can prefer hypothesis-confirming tests unless the reasoning process explicitly encourages disconfirmation.",{},{"id":748,"data":1242,"type":218,"tunes":1244},{"text":1243},"These findings address different phenomena and should not be treated as interchangeable evidence for one universal bias mechanism. Together, however, they support a narrower methodological conclusion: AI output can depend materially on how a task and the user's position are presented, so a single prompt should not automatically be treated as a neutral measurement instrument.",{},{"id":753,"data":1246,"type":42,"tunes":1248},{"text":1247,"level":756},"Selected References",{},{"id":759,"data":1250,"type":277,"tunes":1253},{"meta":1251,"items":1252,"style":276},{},[763,764,765,766,767],{},{"id":770,"data":1255,"type":735,"tunes":1256},{},{},{"id":774,"data":1258,"type":42,"tunes":1260},{"text":1259,"level":247},"Continue the Series",{},{"id":779,"data":1262,"type":277,"tunes":1269},{"meta":1263,"items":1264,"style":276},{},[1265,1266,1267,1268],"\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fbeyond-prompt-engineering-a-methodology-for-more-reliable-ai-reasoning\">Beyond Prompt Engineering: A Methodology for More Reliable AI Reasoning\u003C\u002Fa>\u003C\u002Fb> — the methodological framework behind this series.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fprompt-invariance-does-the-conclusion-survive-the-prompt\">Prompt Invariance: Does the Conclusion Survive the Prompt?\u003C\u002Fa>\u003C\u002Fb> — testing whether conclusions remain stable under blind, inverted and adversarial formulations.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffalsification-for-ai-reasoning-from-answers-to-tested-hypotheses\">Falsification for AI Reasoning: From Answers to Tested Hypotheses\u003C\u002Fa>\u003C\u002Fb> — replacing confirmatory reasoning with competing hypotheses and disconfirming tests.","\u003Cb>\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Ffrom-research-protocol-to-a-general-ai-reasoning-framework\">From Research Protocol to General AI Reasoning Framework\u003C\u002Fa>\u003C\u002Fb> — generalizing the methodology across research, software, debugging and strategic analysis.",{},"2.31.6","Prompt wording is not neutral. Explore how framing, assumptions, instruction-following and sycophancy can shape AI reasoning—and why reliable conclusions require testing beyond the original prompt.",{"lang":7,"title":208,"content":210,"contentJson":1273,"excerpt":789},{"time":212,"blocks":1274,"version":788},[1275,1278,1281,1284,1287,1290,1293,1296,1299,1302,1305,1310,1313,1316,1319,1322,1325,1328,1331,1334,1337,1340,1343,1346,1349,1352,1355,1358,1361,1364,1367,1370,1373,1376,1379,1382,1387,1390,1393,1396,1399,1402,1405,1408,1411,1414,1417,1420,1423,1426,1429,1432,1435,1438,1441,1444,1447,1450,1453,1456,1459,1462,1465,1468,1471,1474,1477,1480,1483,1486,1489,1492,1495,1498,1501,1504,1507,1510,1513,1516,1519,1522,1525,1528,1531,1534,1537,1540,1543,1548,1551,1554,1557,1560,1563,1566,1569,1572,1575,1578,1581,1584,1587,1590,1593,1598,1601,1604],{"id":215,"data":1276,"type":218,"tunes":1277},{"text":217},{},{"id":221,"data":1279,"type":218,"tunes":1280},{"text":223},{},{"id":226,"data":1282,"type":218,"tunes":1283},{"text":228},{},{"id":231,"data":1285,"type":236,"tunes":1286},{"text":233,"caption":234,"alignment":235},{},{"id":239,"data":1288,"type":218,"tunes":1289},{"text":241},{},{"id":244,"data":1291,"type":42,"tunes":1292},{"text":246,"level":247},{},{"id":250,"data":1294,"type":218,"tunes":1295},{"text":252},{},{"id":255,"data":1297,"type":218,"tunes":1298},{"text":257},{},{"id":260,"data":1300,"type":218,"tunes":1301},{"text":262},{},{"id":265,"data":1303,"type":218,"tunes":1304},{"text":267},{},{"id":270,"data":1306,"type":277,"tunes":1309},{"meta":1307,"items":1308,"style":276},{},[274,275],{},{"id":280,"data":1311,"type":218,"tunes":1312},{"text":282},{},{"id":285,"data":1314,"type":42,"tunes":1315},{"text":287,"level":247},{},{"id":290,"data":1317,"type":218,"tunes":1318},{"text":292},{},{"id":295,"data":1320,"type":218,"tunes":1321},{"text":297},{},{"id":300,"data":1323,"type":218,"tunes":1324},{"text":302},{},{"id":305,"data":1326,"type":218,"tunes":1327},{"text":307},{},{"id":310,"data":1329,"type":218,"tunes":1330},{"text":312},{},{"id":315,"data":1332,"type":236,"tunes":1333},{"text":317,"caption":234,"alignment":235},{},{"id":320,"data":1335,"type":42,"tunes":1336},{"text":322,"level":247},{},{"id":325,"data":1338,"type":218,"tunes":1339},{"text":327},{},{"id":330,"data":1341,"type":218,"tunes":1342},{"text":332},{},{"id":335,"data":1344,"type":218,"tunes":1345},{"text":337},{},{"id":340,"data":1347,"type":218,"tunes":1348},{"text":342},{},{"id":345,"data":1350,"type":218,"tunes":1351},{"text":347},{},{"id":350,"data":1353,"type":218,"tunes":1354},{"text":352},{},{"id":355,"data":1356,"type":42,"tunes":1357},{"text":357,"level":247},{},{"id":360,"data":1359,"type":218,"tunes":1360},{"text":362},{},{"id":365,"data":1362,"type":218,"tunes":1363},{"text":367},{},{"id":370,"data":1365,"type":236,"tunes":1366},{"text":372,"caption":234,"alignment":235},{},{"id":375,"data":1368,"type":218,"tunes":1369},{"text":377},{},{"id":380,"data":1371,"type":236,"tunes":1372},{"text":382,"caption":234,"alignment":235},{},{"id":385,"data":1374,"type":218,"tunes":1375},{"text":387},{},{"id":390,"data":1377,"type":218,"tunes":1378},{"text":392},{},{"id":395,"data":1380,"type":218,"tunes":1381},{"text":397},{},{"id":400,"data":1383,"type":277,"tunes":1386},{"meta":1384,"items":1385,"style":276},{},[404,405,406,407,408],{},{"id":411,"data":1388,"type":218,"tunes":1389},{"text":413},{},{"id":416,"data":1391,"type":42,"tunes":1392},{"text":418,"level":247},{},{"id":421,"data":1394,"type":218,"tunes":1395},{"text":423},{},{"id":426,"data":1397,"type":218,"tunes":1398},{"text":428},{},{"id":431,"data":1400,"type":236,"tunes":1401},{"text":433,"caption":234,"alignment":235},{},{"id":436,"data":1403,"type":218,"tunes":1404},{"text":438},{},{"id":441,"data":1406,"type":218,"tunes":1407},{"text":443},{},{"id":446,"data":1409,"type":218,"tunes":1410},{"text":448},{},{"id":451,"data":1412,"type":218,"tunes":1413},{"text":453},{},{"id":456,"data":1415,"type":42,"tunes":1416},{"text":458,"level":247},{},{"id":461,"data":1418,"type":218,"tunes":1419},{"text":463},{},{"id":466,"data":1421,"type":218,"tunes":1422},{"text":468},{},{"id":471,"data":1424,"type":218,"tunes":1425},{"text":473},{},{"id":476,"data":1427,"type":218,"tunes":1428},{"text":478},{},{"id":481,"data":1430,"type":218,"tunes":1431},{"text":483},{},{"id":486,"data":1433,"type":218,"tunes":1434},{"text":488},{},{"id":491,"data":1436,"type":218,"tunes":1437},{"text":493},{},{"id":496,"data":1439,"type":42,"tunes":1440},{"text":498,"level":247},{},{"id":501,"data":1442,"type":218,"tunes":1443},{"text":503},{},{"id":506,"data":1445,"type":218,"tunes":1446},{"text":508},{},{"id":511,"data":1448,"type":218,"tunes":1449},{"text":513},{},{"id":516,"data":1451,"type":218,"tunes":1452},{"text":518},{},{"id":521,"data":1454,"type":218,"tunes":1455},{"text":523},{},{"id":526,"data":1457,"type":42,"tunes":1458},{"text":528,"level":247},{},{"id":531,"data":1460,"type":218,"tunes":1461},{"text":533},{},{"id":536,"data":1463,"type":236,"tunes":1464},{"text":538,"caption":234,"alignment":235},{},{"id":541,"data":1466,"type":218,"tunes":1467},{"text":543},{},{"id":546,"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Ruter prepoznaje SIM karticu i uspešno se povezuje, ali automatsko prebacivanje, oporavak modema, odluke zasnovane na signalu i čista failover logika i dalje zahtevaju dublje testiranje.","\u002Fuploads\u002F2026\u002F06\u002Fopenwrt-router-review-dual-sim-03-1781620592829-7t77j7.webp","2026-06-16T10:40:00.000Z",{"id":1669,"slug":1670,"title":1671,"excerpt":1672,"featuredImage":1673,"publishedAt":1674},"448","google-io-2026-android-xr-and-intelligent-eyewear","Google I\u002FO 2026: Android XR, inteligentne naočare i ambijentalni AI interfejs","Google I\u002FO 2026 je pogurao Android XR i inteligentne naočare od koncepta ka smeru stvarne platforme. Ovaj članak analizira audio-naočare, naočare sa ekranom, kontekstualnu svest koju pokreće Gemini, implikacije za programere, rizike po privatnost i zašto se nosiva veštačka inteligencija manje bavi zamenom telefona, a više kreiranjem ambijentalnih površina za asistenciju.","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-android-xr-and-intelligent-eyewear-1779227942270-dtsm9y.webp","2026-05-21T11:05:00.000Z",{"id":1676,"slug":1677,"title":1678,"excerpt":1679,"featuredImage":1680,"publishedAt":1681},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps: Šta se menja kada je model LLM","MLOps upravlja sistemima mašinskog učenja; LLMOps proširuje te prakse na promptove, kontekst, pretragu, provajdere, alate, evaluacije i ponašanje u vreme izvršavanja oko velikih jezičkih modela.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":1683,"slug":1684,"title":1685,"excerpt":1686,"featuredImage":1687,"publishedAt":1688},"484","what-is-an-ai-platform-architect-models-data-runtime-security-and-operations","Šta je arhitekta AI platforme? 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