Why convert PDFs before using them in RAG?

Cleaner source text can improve chunk boundaries, reduce repeated page furniture, and remove PDF layout noise before embedding or indexing.

Should I use Markdown or plain text for RAG?

It depends on your pipeline. Markdown can preserve headings and list structure, while plain text may be better if you want the simplest possible normalized export.

RAG preparation

Convert PDFs to cleaner text before you chunk, embed, or index them.

RAG systems inherit the quality of the source text they ingest. PDF2X helps you remove repeated page furniture, broken line wraps, and scan noise before those problems spread through your retrieval layer.

Open Converter ChatGPT guide

Think of it as document normalization for the front door of your pipeline.

Why the preprocessing step matters

Cleaner source text pays off before retrieval ever begins.

Reduce retrieval noise

Repeated headers and footers often get copied into many chunks, where they dilute relevance signals and confuse citations.

Repair paragraph flow

Wrapped lines and split words can create poor chunk boundaries and unnatural semantic units.

Keep scans useful

OCR makes scanned documents usable in the same ingestion path as native PDFs, especially when paired with cleanup.

Suggested settings

A simple default profile for indexing and chunking.

Native text PDFs

Start with Text or Auto mode, keep cleanup on, and export to plain text if your chunker does not care about lightweight heading structure.

Scanned PDFs

Use OCR or Auto mode and keep OCR image preprocessing enabled so the extracted text is cleaner before chunking.

Markdown vs plain text for RAG

Markdown is useful when heading structure improves segmentation. Plain text is better when you want the flattest normalized export possible.