How to prepare documents for RAG and AI knowledge bases
Retrieval-augmented generation (RAG) is how most "chat with your documents" tools work: custom GPTs, Claude Projects, NotebookLM, company knowledge bots and anything built on a vector database. Instead of reading everything at once, the system splits your documents into chunks, finds the chunks most relevant to a question, and hands only those to the AI.
That means answer quality depends heavily on what goes in. A messy PDF produces messy chunks, and the right passage never gets found. This checklist covers the preparation that makes the biggest difference.
1. Convert everything to clean text first
Most RAG tools accept PDFs directly, but they extract the text themselves, often without OCR and without much care for layout. Converting to Markdown yourself gives you control:
- Scanned pages get proper OCR in the right language.
- You can see exactly what text the system will index.
- Word files keep their headings and tables.
Drop the files on the Docuflat converter and download the Markdown. The conversion happens in your browser, which matters if the documents are confidential.
2. Give every document real headings
Chunking tools often split on headings, and many include the heading with each chunk. A chunk that starts with ## Refund policy > Annual plans is much easier to retrieve, and to understand, than an anonymous paragraph.
- Use one
#title per document. - Use
##and###for sections, in a logical order. - For PDF conversions, replace the
## Page Nmarkers with meaningful section headings.
3. Remove the noise
Anything that repeats across pages pollutes search results:
- Running headers and footers, page numbers, "Confidential" stamps.
- Tables of contents and indexes (they match every query but answer none).
- Legal boilerplate copied into every document, unless people will ask about it.
- Blank form fields and signature blocks.
4. Make tables self-explanatory
Tables are often split across chunks, and a row of numbers without its header means nothing.
- Keep tables as Markdown tables with a header row.
- Split very large tables into smaller ones by topic or period, each with its own heading.
- Add a sentence above each table saying what it shows and its units, such as "Prices in INR, including GST".
5. Put key facts in words
Retrieval matches the question against text. If the answer lives only in a chart, an image or a footnote, it won't be found. For the facts people will ask about, write them out in a sentence near the relevant heading.
6. Add context to each document
At the top of each file, note what it is, who it's from, when it applies and whether it replaces something:
# Leave policy
Applies to: all employees in India. Effective: 1 April 2026. Replaces the 2024 policy.This helps the system choose between similar documents, and helps the AI answer "which version applies?"
7. One topic per file, sensible size
Very long files containing unrelated topics make retrieval noisier. Split a 200-page manual into chapters, each as its own file with a clear title. Merge tiny related snippets into one file.
8. Remove outdated and duplicate versions
If three versions of a policy are indexed, the AI may quote the wrong one. Keep only the current version, or label old ones clearly as superseded.
9. Test with real questions
Before rolling it out, ask 10–20 questions you know the answers to:
- Did it find the right document and section?
- Did it quote the right numbers?
- Where it failed, was the information missing, buried in noise, or split badly?
Fix the source documents and test again. That loop improves results more than any setting in the tool.
Summary
Convert to Markdown, add real headings, strip repeated noise, make tables and key facts self-explanatory, and keep one current version of each document. Your RAG system can only find what you've made findable.