Using QA pairs to make RAG over PDFs cleaner and cheaper
Large technical PDFs can still be hard to answer questions from, even when OCR and PDF parsers extract text, layout, tables, and some images fairly well. Useful evidence may be broken across chunks, mixed with noise, split away from related details, or hidden in figures and tables that the retriever does not handle well.
A practical approach is to create QA pairs from the document and treat them as a structured middle layer, not just as final answers. The pipeline is: read the PDF or document with OCR or a PDF parser, turn it into markdown or layout JSON, split it into chunks, clean and normalize it, generate QA pairs or VQA pairs, filter and evaluate them, then use them for RAG or .
Noisy chunks can become cleaner knowledge snippets. Long documents can become many grounded QA pairs, and multi-hop QA pairs can reveal relationships that simple chunk may miss.
Key points
- OCR and alone may not make large technical documents easy to search.
- QA pairs can work as intermediate search data, not only as final answers.
- The suggested flow is , chunking, cleaning, QA generation, filtering, , then RAG or training use.
- Cleaner QA pairs can help an agent read less irrelevant context.
- VQA pairs can help capture information from figures and tables that text misses.