RAG: enrich AI with your own knowledge base

In a previous post, we saw that a custom AI agent rests on three capabilities: know, look up, and act. Let's start with the first: how do you get AI to truly know your business? The answer comes down to one acronym: RAG.

The problem with generic AI

A large language model has been trained on a huge amount of public text. It "knows" a great many general things, but it doesn't know your prices, your return policy, your spec sheets, or your internal FAQ — in other words, it's unaware of your knowledge base, the body of documents specific to your business.

Worse: if you ask it a question it doesn't know the answer to, it may "make up" a plausible but false answer. This is called a hallucination — and it's obviously unacceptable when a customer expects reliable information about your products or services.

What RAG means

RAG stands for "Retrieval-Augmented Generation." The name sounds technical, but the idea is simple: instead of asking the model to answer from memory, we first give it the right documents, then ask it to answer based on them.

Concretely, the process happens in four steps:

1. We gather your knowledge base: product sheets, guides, policies, FAQs, procedures, and so on.
2. We split these documents into small passages and index them in a vector store, which lets us retrieve information by meaning, not just by keyword.
3. When a customer asks a question, we first retrieve the few most relevant passages from that store.
4. We hand those passages to the model and ask it: "Answer the customer's question based only on this information."

The model then generates an answer grounded in the real documents of your knowledge base.

The concrete benefits

• Answers reflect your up-to-date information, not generalities.
• The risk of hallucination is greatly reduced.
• The answer can cite its source ("according to our return policy…"), which builds customer trust.
• Updating your knowledge base doesn't require retraining a model: you just add or correct a document.

In short, RAG turns an assistant that "knows the world" into an assistant that "knows your business."

What's next?

RAG is ideal for information that changes slowly: policies, descriptions, procedures. But what about data that changes minute to minute, like a product's stock quantity? That's the subject of another post: tools.

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