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Building a Retrieval Index for Your Own Documents

AI Tutorials · By: AIReport Admin · 2026-09-06 · 👁 4.4k
Building a Retrieval Index for Your Own Documents

Chunking strategy decides most of your answer quality. Here is a workable approach for PDFs, Markdown and web pages.

Retrieval systems live or die on how documents are split. Get chunking right and a modest model performs well; get it wrong and no amount of prompt tuning recovers it.

Chunking rules that work

  • Split on structure, not character count. Headings, list boundaries and paragraph breaks are natural units.
  • Keep headings with their content. Prefix each chunk with the heading path so a retrieved passage carries its context.
  • Overlap slightly. A small overlap prevents answers that straddle a boundary from being cut in half.
  • Never split a table. Convert it to a compact textual form or keep it whole, even if the chunk grows.

Choosing a size

Aim for chunks in the range of one to three paragraphs. Smaller chunks retrieve more precisely but lose surrounding explanation; larger chunks retrieve noise. Test both on your own question set — the right answer depends on your documents.

Metadata is not optional

Store source file, section title, last-modified date and a URL with every chunk. You need it for citations, for filtering by recency, and for deleting stale content when source documents change.

Refresh on a schedule

An index that is not refreshed slowly becomes wrong. Tie reindexing to your source system's update events, or rebuild nightly if events are unavailable.

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