Pruning RAG context down to what the answer actually needs
Kapa has developed a method to prune RAG context by using a smaller, cost-effective LLM to filter out irrelevant retrieved chunks before sending them to a larger model. This approach reduces query costs by a third while maintaining high recall accuracy.
Why it matters
Optimizing RAG pipelines is critical for businesses looking to scale AI assistants while managing the high costs associated with large context windows.
Pruning agent context down to what the answer actually needs, while keeping 96% of recall
The content is a technical case study focused on engineering efficiency without political bias.
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