AI & LLMs
Retrieval-augmented generation (RAG)
Also known as: RAG
RAG fetches relevant documents at query time and feeds them to a language model as context, so answers are grounded in your data instead of only the model's training.
Retrieval-augmented generation (RAG) combines a retriever with a generator. When a question comes in, the system first retrieves relevant passages - usually via vector search over embeddings - then passes them to a language model as context so its answer is grounded in those sources.
RAG is the common alternative to fine-tuning when you need a model to answer over private, large, or frequently changing data without retraining it.
In GraphCanon
Retrieval, vector database, and framework tools that implement RAG pipelines are a core slice of the graph. Compare pages and stacks show how these pieces (embeddings, a vector store, an orchestration framework) fit together.
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Related terms
Last reviewed 2026-07-09