Definition
Retrieval-augmented generation (RAG) is a technique that lets an AI model answer questions using your own documents and data. At query time it retrieves the most relevant content from your knowledge base and feeds it to the model as context, so the model can respond with your information without being retrained on it.
How RAG works
A RAG system indexes your documents into a searchable store. When a user asks a question, the system finds the most relevant passages, hands them to the language model alongside the question, and the model answers grounded in that retrieved content. The result is an assistant that can cite your policies, contracts, or product docs accurately.
Why it matters for business AI
RAG is how most practical business AI gets access to private, current information. It avoids the cost and staleness of retraining a model, keeps sensitive data in your control, and lets answers cite a source - which reduces hallucination and builds trust. It is a core building block of custom AI agents that need to reason over a firm's own knowledge.
Frequently Asked Questions
How is RAG different from fine-tuning a model?
Fine-tuning changes the model's weights by training it on your data - expensive and slow to update. RAG leaves the model unchanged and instead supplies relevant data at query time. RAG is easier to keep current and keeps your data out of the model itself.
Does RAG stop AI from making things up?
It reduces it substantially by grounding answers in retrieved source content and enabling citations, but it does not eliminate hallucination entirely. Answer quality still depends on the quality of your indexed content and the retrieval step.
Do we need RAG to use AI on our documents?
For most cases where an AI needs to answer from your private, frequently changing documents, RAG is the standard and most cost-effective approach - more practical than retraining a model each time your content changes.
Put this into practice
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