English(EN) Context engineering is mostly deciding what to leave out

LLM上下文工程：少即是多，以获得更好的结果

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-12 03:02

大型语言模型的上下文工程通常侧重于增加可处理的信息量，但通过减少上下文，实际结果常常得到改善。不相关或嘈杂的信息会分散模型的注意力，导致答案变差，即使正确的信息在技术上存在。战略性地修剪上下文，专注于最相关的事实和指令，以及在上下文窗口内仔细排序信息，可以显著提高答案质量和成本效益。 AI

影响优化上下文窗口的使用可以带来更准确、更具成本效益的LLM应用。

排序理由该条目是一篇讨论使用LLM最佳实践的观点文章，而非发布或研究发现。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 6 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 该条目是一篇讨论使用LLM最佳实践的观点文章，而非发布或研究发现。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

dev.to — LLM tag TIER_1 English(EN) · frank chu · 2026-09-12 03:02

上下文工程主要是决定剔除什么

<p>Most advice about context windows is about fitting more in. Bigger windows, more retrieved documents, the whole file instead of the function. In practice the thing that has improved my results the most is the opposite move: getting the junk out. A window packed with marginally…