Building & tuning

What is retrieval-augmented generation?

RAG is how you get an AI assistant that knows your policies, contracts, or product docs without retraining a model. It is the default approach for most business knowledge tasks.

In plain terms

Retrieval-augmented generation lets an AI model answer using your own documents by fetching the relevant passages at the moment of the question.

Also called RAG.

Retrieval-augmented generation, or RAG, means the system looks up your documents first and then writes the answer from what it found. The model supplies the sentences. Your files supply the facts. That is how a chat over your policies stays closer to what the policies actually say.

The lookup is the part that fails quietly. If the search returns the wrong page, the model writes a fluent answer from the wrong page. You judge RAG by the sources it shows, not by how polished the paragraph is. Start with one trusted library, such as approved policies, and ask the system to say when it found nothing.

Think of it this way: Without RAG, an LLM is a well-read expert answering from memory. With RAG, that expert pulls the relevant pages from a file cabinet before answering, so the answer is grounded in your actual documents.

An insurance company builds a claims assistant that searches their policy library in real time. Adjusters get answers grounded in the exact policy version, reducing escalations and errors.

A bank's staff ask the intranet why a wire was held. The system searches the current payments manual, quotes the rule, and links the paragraph. When the manual has no rule for that country, it says it could not find one, instead of inventing a reason.

Whenever the AI needs to answer from your specific content: policies, product docs, contracts, knowledge bases. It is the default architecture for enterprise knowledge work. When the task does not require specific factual retrieval, such as creative drafting or general summarization. Adding retrieval to those tasks adds latency and cost without meaningful benefit.

RaftLabs points the model at your documents and your rules, then checks the answers against cases you already trust. That surrounding work is where these projects succeed or stall. The related work on our side is RAG development.

This sits with the other building & tuning terms on the glossary. How a general model gets pointed at your documents, your tone, and your workflow. Worth reading next: Fine-tuning, Prompt Engineering, and Embeddings. For the full walkthrough, read What is retrieval-augmented generation? The full guide.

Common questions

No. Training changes the model with past examples. RAG leaves the model as it is and hands it the relevant pages at question time. The pages can be updated tomorrow without a retraining project. It only works if those pages are current and the search can find the right one.
Pick twenty questions staff actually ask. Check whether the right document was retrieved and whether the answer matches that document. Also ask a question the library cannot answer. The system should admit the gap. A confident answer with no source is a miss, even if it sounds right.

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