English(EN) I crawled 150 pages from 6 popular docs sites into RAG chunks: one page often eats half the token budget

单个文档页面可能消耗RAG Token预算的一半

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-03 17:46

最近对六个热门文档网站的爬取显示，单个页面可能会消耗检索增强生成（RAG）系统Token预算的很大一部分。分析发现，版本历史页面、大量表格和教程通常会占据Token数量的大头。此外，翻译成多种语言的文档可能会不成比例地增加爬取量，即使目标输出语言是英语，非英语版本也可能被索引。作者建议过滤掉更新日志、发布说明和大型页面，并指定目标语言，以优化RAG索引。 AI

影响通过过滤大型页面和翻译来优化RAG索引，可以提高检索准确性并降低成本。

排序理由对常用文档网站进行RAG索引分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

基础设施

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: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对常用文档网站进行RAG索引分析。[lever_c_demoted from research: ic=1 ai=1.0]
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: infra; 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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · Fast Data · 2026-11-03 17:46

我将6个热门文档网站的150页内容爬取并切分成RAG块：一页常吃掉一半的token预算

<p>If you build a RAG bot on top of someone's documentation, the first crawl decides what your retriever will see. I wanted to know what a naive crawl of popular docs sites actually returns, so on 3 November 2026 I pointed a crawler at six of them and counted.</p> <p><em>Disclosu…

本文如何被排名

报道来源 [1]

我将6个热门文档网站的150页内容爬取并切分成RAG块：一页常吃掉一半的token预算

相关实体

相关话题