How we taught a small LLM to throw away 68% of our RAG context - kapa.ai - Instant AI answers to technical questions
Jul 2, 2026
How we taught a small LLM to throw away 68% of our RAG context
Pruning agent context down to what the answer actually needs, while keeping 96% of recall
by
Lars Baltensperger
Kapa builds AI assistants that answer complex questions over large product knowledge bases. Think technical documentation, API references, PDFs, forums, support threads. Developers use our retrieval API to give their agents context about their product, and the same retrieval layer powers our end-to-end assistants.
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. Our retrieval comes in several forms, (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.kapa.ai/blog/how-we-prune-rag-context)获取最准确的信息。

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🔗 **原文链接**: [Pruning RAG context down to what the answer actually needs](https://www.kapa.ai/blog/how-we-prune-rag-context)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 37票 · 👤 emil_sorensen

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-07-07 08:01