GUIDE
What is RAG?
RAG, or retrieval-augmented generation, is a way to make an AI answer from your own documents instead of from memory. It retrieves the relevant material first, then answers with the source attached.
Last updated October 2026.
How it works
Your documents are split into chunks, turned into embeddings, and stored in a vector database. When someone asks a question, the system searches for the chunks most relevant to it, sends those chunks to the model alongside the question, and the model answers using that material. The answer can point back to the source.
Why it matters
A model on its own answers from what it learned during training, which does not include your business. RAG changes the source of truth: the answer comes from your documents, your policies, and your data, and it can show where each answer came from. That is the difference between an assistant people trust and one they stop using.
When it is worth building
- Knowledge is spread across documents that are hard to search.
- People ask the same questions and wait for a person to answer.
- Answers need a source someone can check.
- Access rules differ across teams or documents.
When it is not
If a fixed rule already answers the question, you do not need RAG. If the content changes every minute, a database query is better. RAG earns its place when the answer lives in text that a person would otherwise have to read.
Related
See how I build these systems on the RAG development service page, or read RAG vs fine-tuning.
Thinking about a knowledge assistant?
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