Single documentation pages can consume half of RAG token budget

A recent crawl of six popular documentation sites revealed that a single page can consume a significant portion of a retrieval-augmented generation (RAG) system's token budget. The analysis found that version history pages, extensive tables, and tutorials often dominate the token count. Additionally, documentation translated into multiple languages can disproportionately inflate the crawl size, with non-English versions potentially being indexed even when English is the desired output language. The author recommends filtering out changelogs, release notes, and large pages, as well as specifying the desired language, to optimize RAG indexing. AI

IMPACT Optimizing RAG indexing by filtering large pages and translations can improve retrieval accuracy and reduce costs.

RANK_REASON Analysis of common documentation sites for RAG indexing. [lever_c_demoted from research: ic=1 ai=1.0]

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