RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an AI technique that retrieves relevant information from a knowledge base and feeds it to a language model before it answers. This grounds responses in real, up-to-date data instead of the model's memory — dramatically reducing hallucinations.
In plain English
Think of a closed-book exam versus an open-book one. A plain LLM answers from memory and sometimes makes things up. RAG hands the model the right pages of your documents first, so it answers from the source instead of guessing.
How it works
- 1Your documents are split into chunks and converted to embeddings (vectors) stored in a vector database.
- 2When a user asks a question, it's embedded and used to search for the most relevant chunks.
- 3Those chunks are inserted into the model's prompt as context.
- 4The model generates an answer grounded in that retrieved context, often with citations.
A support chatbot that answers from your actual help docs and product manuals — so it says 'your plan allows 5 seats' by reading your real pricing page, not by guessing.
Why it matters
RAG is essential whenever an AI feature needs to answer from private, current, or domain-specific data — internal wikis, product docs, legal contracts, or customer records — that the base model was never trained on.
Related
RAG — FAQs
Does RAG stop hallucinations completely?
It reduces them substantially by grounding answers in real sources, but doesn't eliminate them entirely. Good RAG systems add citations and guardrails so answers can be verified.
Is RAG better than fine-tuning?
They solve different problems. RAG injects fresh, factual knowledge at query time; fine-tuning changes the model's style or behaviour. Many production systems use both.
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