How kapa.ai used a small LLM to prune 68% of RAG context while retaining 96% recall
Kapa.ai describes their approach to optimizing RAG (Retrieval-Augmented Generation) for technical Q&A systems. They developed a method using a small LLM to prune retrieved context chunks, discarding 68% of irrelevant context while maintaining 96% recall. The article details their iterative process of building a pruning agent that evaluates each chunk's relevance to the specific question, significantly reducing token usage and improving answer quality by removing noise from the context window.
Key quotes
For all the debate in 2026 about whether agents still need RAG, in our domain nothing comes close when knowledge bases get large and complex.
We found that by pruning away irrelevant context, we could reduce the context size by 68% while maintaining 96% of the recall.
The key insight was that a small, focused LLM could be more effective at judging relevance than relying solely on embedding similarity scores.
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