What Is Retrieval-Augmented Generation (RAG)? A Plain-English Guide
Key takeaway
RAG makes an LLM answer from your own trusted documents instead of only its training data. It retrieves the most relevant passages, feeds them to the model, and generates a grounded answer — dramatically reducing made-up responses.
Large language models are fluent but don't inherently know your business. Retrieval-augmented generation (RAG) closes that gap by giving the model your content at answer time.
How RAG works, step by step
- Your documents are split into chunks and indexed (usually as embeddings in a vector store).
- When a user asks something, the system retrieves the most relevant chunks.
- Those chunks are added to the prompt, so the model answers from real, cited source material.
Why it matters
RAG grounds answers in facts you control, which sharply reduces hallucinations, lets you cite sources, and keeps responses current without retraining the model. It's the backbone of most production document-Q&A and support assistants.
When you need it
If users ask questions whose answers live in your docs, policies, or product data, you almost certainly want RAG. We design and build RAG systems that stay accurate and maintainable as your content grows.