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PRACTICAL GUIDE · Knowledge & RAG

Handling scanned PDFs in a knowledge pipeline

A scanned document may need reliable text extraction before retrieval can work. Poor extraction can silently corrupt dates, numbers and tables.

A practical starting point

Work through these checks in order. Use the results to decide what needs to change in your own workflow.

  1. Inspect extracted text from representative pages.
  2. Check headings, reading order and important numerical fields.
  3. Flag low-quality documents for correction rather than indexing them uncritically.
ILLUSTRATIVE EXAMPLE

A scanned table with shifted columns can associate the wrong price with a product.

A mistake to avoid

Do not treat successful file upload as proof that usable text was extracted.

How to check progress

Measure extraction errors on fields that matter to the intended questions.

Your action checklist

Use this checklist while reviewing your own setup. Checkmarks are temporary and stay in this open page.

Need the wider workflow?

This quick note covers one decision. For a worked example, decision table and a fuller implementation workflow, read When retrieval is the right addition to an AI assistant.

Further reading

Official background resources for this topic. The workflow above is general guidance, not a guarantee of a particular result.

AI-assisted editorial content. Examples are illustrative, not client case studies. Read our editorial policy or report a correction.