What Is RAG? The Simplest Explanation of How It Works

RAG sounds complicated because the name is complicated. The idea is not. RAG simply means: before the AI answers, it first looks up relevant information from a knowledge source and gives that information to the language model.

Think of an LLM as a smart person sitting at a desk. RAG is the librarian who brings the right page from the right book. The LLM then reads that page and answers you.

First: what does the LLM do?

The LLM is the part that understands language and produces language. It can read your question, understand instructions, compare information, explain something and write an answer.

But the LLM does not automatically know what is currently inside your company database, your game session, your private documents or a file you created five minutes ago.

It only knows what is already inside the model plus whatever information the application gives it in the current request.

Then: what is the knowledge base?

A knowledge base is simply information the application can search.

It could contain PDFs, manuals, product documentation, support articles, contracts, game rules, weapon data, internal company documents, database records or other text.

The knowledge base can be local on your own machine. It can be on a server. It can be in a vector database. It can also be built from normal files. RAG does not mean Internet.

So what does RAG actually do?

That is RAG.

The full name is Retrieval-Augmented Generation. Retrieval means finding the relevant information. Augmented means adding that information to the model's context. Generation means the LLM writes the final answer.

A very simple example

Imagine you have a local knowledge base about a game.

You ask: “Which ammunition does the AKM use?”

RAG searches the knowledge base and finds the entry about the AKM. It gives that small piece of information to the LLM. The LLM then answers: “The AKM uses 7.62 mm ammunition.”

The LLM did not need the entire database. RAG only brought the useful part.

Now the important part: RAG is not the current state

This is where many explanations become confusing.

RAG usually gives the AI knowledge. A state system gives the AI facts about what is true right now.

What is a state database?

A state database or state store is simply a place where the application keeps current facts.

In a game, the engine already knows things such as your health, position, inventory, ammunition, current mission, nearby objects and enemy status. An AI system can expose selected parts of that state to the model.

In a business application, the same idea could be an order database, a customer record, a project status or the current value of a sensor.

The state is created by the application itself as things happen. If you lose health, the game updates the health value. If you pick up ammunition, the inventory changes. If an order is paid, the business system changes the order status.

How the three pieces work together

So the basic architecture is:

Does RAG always use a vector database?

No.

A vector database is a common way to build semantic search, but it is not the definition of RAG.

The important part is retrieval: the system finds relevant external information and adds it to the LLM's context before the answer is generated.

OpenAI's File Search, for example, can work with files stored in vector stores. Files are chunked into smaller pieces so the system can retrieve the parts that are relevant to a question. That is one implementation of the same basic idea.

What is an embedding, in plain English?

You do not need to understand embeddings to understand RAG.

But the simple version is this: an embedding is a numerical representation of meaning. It helps a search system find text that is conceptually similar even when the words are not exactly the same.

For example, a normal keyword search may look for the exact words “car repair.” Semantic search can also understand that “fix my vehicle” is about a similar topic.

That makes embeddings useful for RAG, but RAG can also use keyword search, database queries or a hybrid of several methods.

RAG is not memory either

Memory is another concept that is often mixed together with RAG.

Memory is usually information the system keeps about previous interactions or previous events. RAG is the mechanism used to retrieve relevant knowledge when it is needed.

A real game example: PUBG Ally

PUBG Ally is a useful example because it makes the difference visible.

KRAFTON describes live match state as a separate source of truth. The game exposes current facts through observation tools: current weapon, ammunition, health, safe-zone status, nearby items and combat situation.

Knowledge lookup is a different job. The system can use curated knowledge about weapons, attachments, items and rules. NVIDIA's ACE Game Agent SDK also exposes a separate RAG API for retrieving knowledge from developer-built databases.

That gives us the clean separation: the game engine says what is happening now, retrieval provides relevant knowledge, and the language model decides what the information means.

One complete example

Imagine you tell an AI teammate: “I am low on health. Should we attack?”

RAG did not control the character. The state database did not reason. The LLM did not directly change the game. Each part had one job.

Why use RAG at all?

Because putting every document, rule and database record into every prompt would be slow, expensive and often confusing.

RAG lets the system select only the information that is useful for the current question.

It also lets you update the knowledge base without retraining the entire language model. Change the document or database, rebuild or refresh the index when necessary, and the next retrieval can use the newer information.

What RAG does not guarantee

RAG can improve grounding, but it does not make an answer automatically correct.

The retrieval step can find the wrong document. The correct document can be outdated. The LLM can misunderstand good evidence. Or the current state can have changed.

A reliable system therefore has to validate retrieval, state freshness and the model's final reasoning separately.

The easiest mental model to remember

Conclusion

RAG is much less mysterious once the parts are separated.

The LLM understands and generates language. The application maintains current state. The knowledge base stores information. RAG finds the useful part of that information and puts it into the LLM's context. Tools or the application perform real actions.

That is the basic architecture behind many modern AI assistants and agents.

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