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AI Integration

The Unglamorous Work That Decides Answer Quality

Preparing documents for AI retrieval: chunking on meaning, attaching metadata, deleting superseded versions, handling awkward formats and assigning owners.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Answer quality tracks document quality almost exactly. Split on meaning rather than length, attach metadata, remove superseded versions, and give every document an owner and a review date.

Split on meaning, not on character count

Naive splitting every thousand characters cuts tables in half, separates headings from content and leaves passages that make no sense alone.

Split on structure — sections, headings, natural boundaries — and each retrieved passage stands on its own, which is what the model needs.

Attach metadata to everything

  • Document title and section heading, carried with every passage
  • Last reviewed date, so stale content can be down-weighted
  • Owner, so somebody is accountable for accuracy
  • Audience or permission level, for filtering
  • Product, region or entity, where you have more than one
Metadata is what lets retrieval prefer the current UK policy over the superseded US one. Without it, both look equally relevant.

Delete rather than archive

Superseded documents that remain in the index are the largest single cause of confidently wrong answers. Version 3 and version 5 of a policy look equally authoritative to a retrieval system.

Remove them from the index properly. Keep them in your archive if you must, well away from anything the assistant can see.

Handle the difficult formats deliberately

FormatHandling
Well-structured proseStraightforward
PDFs with real textGood, with layout care
Scanned PDFsNeeds OCR, quality varies
SpreadsheetsConvert to described rows or summaries
Slide decksOften poor — the meaning is in the speaker

Give every core document an owner

Twenty documents with named owners and review dates will outperform two hundred orphaned ones within a year, because the twenty stay true.

This is the maintenance that keeps a retrieval system useful, and it is organisational rather than technical.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

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Ask about your project

How long does preparation take?

One to three weeks for a typical business knowledge base. It is the step that most determines the outcome and the one most often rushed.

Can the AI clean up our documents?

It can draft summaries and flag contradictions, which helps. Deciding what is currently true still needs a person who knows.

What about documents in email attachments?

Consolidate them into a proper store first. Retrieval over an inbox is a permissions problem waiting to happen.

Do we need a document management system?

Not necessarily. A well-organised shared drive with clear ownership works perfectly well for most businesses.

Keep reading

More on AI Integration

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Documentation scattered across drives and inboxes?

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