RAG - retrieval-augmented generation - is the technique that lets an AI answer from your documents. The system first retrieves the relevant passages from your data, then the language model writes an answer grounded in them, usually with references. It is how "chat with your company data" works - and the practical alternative to retraining a model.
The idea in one paragraph
The term comes from a 2020 research paper by Meta AI (then Facebook AI) and university researchers. Instead of forcing everything the AI knows into the model itself, you keep knowledge in a searchable store. For each question, the system finds the most relevant passages and hands them to the model with the instruction: answer from this.
Why it beats retraining for company data
Your data changes daily. Retraining a model is slow and expensive; retrieval reads the current version of a document at question time.
Your data stays in your systems. With retrieval, documents live in your store under your access rules - they are not baked into a model.
Answers can show their sources. Because the answer was built from retrieved passages, the system can link back to the exact document.
What decides whether answers are good
RAG quality is retrieval quality. If the right paragraph is not found - because the document is missing, badly titled or contradicted by an older copy - the answer will be wrong or vague, confidently. Clean, current, well-named documents matter more than the choice of model.
Where you already meet RAG
Microsoft 365 Copilot, Gemini in Google Workspace and Notion AI all work this way: they search your files first, then answer. If a vendor promises an AI that "knows your company", retrieval is almost always the mechanism underneath.
The limits to know before you buy
It cannot fix missing or outdated content.
It respects - and therefore exposes - your existing file permissions (see our post on AI and your Drive or SharePoint).
Long tables, scanned PDFs without text, and messy folder structures are still hard.
Sources
Lewis et al., "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (2020)
IBM, "What is RAG (Retrieval-Augmented Generation)?" (accessed 20 October 2026)
Last updated: 20 October 2026.
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