What is Retrieval-augmented generation (RAG)?
Verified 5 August 2026
The standard pattern for grounding a model: retrieve, assemble context, generate. It addresses staleness and specificity, and it makes answers checkable because the sources are known.
In practice the retrieval half is where quality is won or lost, and most of the work is not retrieval at all — it is deduplication, trust assessment, reranking and truncation to fit a budget. That loop is expensive in exactly the way that is easy to overlook.
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Common questions
What is Retrieval-augmented generation (RAG)?
RAG retrieves relevant documents and places them in a model’s context so it can answer from current, specific sources rather than from training data alone.
How does Retrieval-augmented generation (RAG) work in practice?
In practice the retrieval half is where quality is won or lost, and most of the work is not retrieval at all — it is deduplication, trust assessment, reranking and truncation to fit a budget. That loop is expensive in exactly the way that is easy to overlook.
See it working
The free tier is 10,000 requests a month with no card — enough to test any of this against your own queries.