RAG (retrieval-augmented generation)
Retrieval-augmented generation is a technique where relevant documents are fetched from an external store and inserted into a language model’s prompt, so the model answers from that specific source material rather than from its training data alone.
RAG is the standard way to make a model answer about private or current information. Most RAG failures are retrieval failures, not generation failures: the model answered faithfully from the wrong passage. This is why retrieval quality should be measured separately from answer quality — otherwise you tune the prompt to fix a chunking problem.
Related terms
- Agentic RAG
Agentic RAG is a retrieval-augmented generation architecture in which the model decides when and what to retrieve — issuing its own queries, evaluating results, and retrieving again — rather than receiving a single fixed retrieval before generating.
- Vector database
A vector database stores text as numerical embeddings and retrieves entries by semantic similarity rather than keyword match, forming the retrieval layer of most RAG systems.
- Chunking
Chunking is the process of splitting source documents into smaller passages for embedding and retrieval, and the chunk size and boundary strategy largely determine how well a RAG system can find relevant information.
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