Pruning RAG context down to what the answer actually needs
kapa.ai
July 6, 2026
8 min read
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48/100
Summary
Kapa.ai has developed a method to prune 68% of irrelevant context from their retrieval-augmented generation (RAG) system while maintaining 96% recall accuracy. Their AI assistants utilize a retrieval API to access and process information from extensive product knowledge bases, including technical documentation and support threads.
Key Takeaways
- A small LLM was implemented to prune 68% of irrelevant context from retrieved chunks while maintaining 96% recall in a retrieval-augmented generation (RAG) system.
- The pruning process reduces query costs by approximately one-third, as the majority of retrieved chunks contribute significantly to the overall expense.
- Traditional reranking methods were found inadequate for determining the relevance of chunks, as they do not consider the contextual relationship between multiple chunks in answering a question.
- The retrieval system employs a three-step process: a retriever identifies relevant documentation, a pruner discards unnecessary chunks, and a generator formulates the final answer.
