Definition
Retrieval-augmented generation (RAG) is a technique that gives a large language model access to your own information at the moment it answers. When a question arrives, the system first searches a knowledge base for the most relevant passages, then places them in the prompt and asks the model to answer using that evidence. The model's general language skill is combined with your specific, current facts, without retraining anything.
In a company, retrieval-augmented generation is how an assistant answers questions about your product manuals, contracts, policies, tickets or internal wiki. Documents are split into chunks, converted into embeddings and stored in a vector database. At query time, the closest chunks are retrieved and cited. This makes answers traceable to a source, keeps knowledge up to date by simply re-indexing, and keeps private data out of model training.
RAG is the default architecture for enterprise AI knowledge tools and has matured with hybrid search, re-ranking and agentic retrieval, where the model decides what to search and when. Larger context windows reduced but did not remove the need for it. The misconception is that RAG is plug and play. Retrieval quality, document hygiene and evaluation decide whether the assistant is trusted or ignored.
In practice
A machinery maker gives field technicians an assistant built on retrieval-augmented generation over 15 years of service manuals. A technician asks about a fault code and gets the exact procedure, with the page cited.
Why it matters
RAG is the fastest, safest way to put your company's knowledge behind an AI assistant. It is where most of the real business value of LLMs sits today.
Frequently asked questions
- What is the difference between RAG and fine-tuning?
- RAG supplies relevant documents to the model at query time, so knowledge stays current and answers can cite sources. Fine-tuning changes the model by training it on examples, which shapes style and behaviour but is poor at storing changing facts. For company knowledge, start with RAG.
- Does RAG stop AI hallucinations?
- It reduces them significantly by grounding answers in retrieved text and lets you show sources, but it does not eliminate them. If retrieval returns the wrong passage or nothing relevant, the model may still improvise. Good systems instruct the model to say when the evidence is missing.