The short answer
Retrieval-augmented generation combines search with generation so answers can use a product's documents, data, or current sources.
RAG is a pattern for grounding generation in information outside the model's original training data.
A system retrieves relevant material, places it in the model's context, and asks the model to answer using that evidence.
Generation cannot compensate for poor retrieval. Teams need to test which sources are selected, how citations are shown, and what happens when evidence is missing.
A useful note, once a week
Make room for better signal.
About the author
Rowan studies software tools and the operating habits that make them useful.
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A practical definition of retrieval-augmented generation when the system can choose how to find and use context.
Vector search is only one part of retrieval. The right database depends on filters, freshness, scale, and operational comfort.
Framework choice matters less than the retrieval, citation, and evaluation practices built around it.
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One thoughtful note on AI, agents, and AGI. No noise.
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