Encyclopedia
Retrieval-augmented generation (RAG)
What retrieval-augmented generation is, the retrieve-then-generate steps, and why it matters for accurate answers.
Retrieval-augmented generation (RAG) is a technique where an AI model first retrieves relevant information from a knowledge source and then generates its answer from that retrieved content, instead of relying only on what it learned during training.
The two steps
- Retrieve: given a question, the system searches a knowledge base and pulls the most relevant passages.
- Generate: the language model writes its answer using those passages as the source, so the reply reflects the company's real content.
Why it matters
A language model on its own only knows its training data, which is general and frozen in time. RAG lets it answer from a specific, up-to-date knowledge base without retraining the model. That grounding is what makes an AI agent accurate about one company and is a main defence against hallucination.
From reference to practice
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