RAG & Retrieval · Ground answers in evidence
How do you parse documents for a RAG pipeline?
Short answer
Extract clean text and structure, not just characters. Preserve headings, lists, tables and page numbers, and test parsing on the formats your users actually upload.
Parsing is where many RAG failures begin. A PDF extractor that drops table structure or merges columns produces chunks that are semantically wrong before retrieval even starts. The parser's output is the ceiling for answer quality.
- Use format-specific parsers: HTML to structured text, PDFs with layout awareness, DOCX with styles.
- Keep headings, lists, tables and page references as metadata or markup.
- Handle scanned documents with OCR and flag low-confidence text.
- Remove navigation, headers, footers and boilerplate that repeat on every page.
Follow-ups you should expect
Interviewers use these to check whether the first answer was understanding or memorisation.
- 1How would you preserve a table's meaning through chunking?
- 2What would you do with a scanned PDF with no text layer?
- 3How do you detect a parser regression?
Say it in the interview
I treat parsing as the foundation. The parser has to preserve structure, not just extract characters, because a flattened table or a merged column produces chunks that cannot answer the question. I keep headings, page numbers and table markup, use OCR where needed, and strip repeated boilerplate. Then I inspect the parsed text on real documents, since a pipeline can ingest everything successfully and still produce unusable content.
Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.
Related questions
Reading is step one
Now say it out loud.
Understanding an answer and delivering it under pressure are different skills. Rehearse this topic in a live RAG & Retrieval mock interview and get scored feedback on depth, clarity and confidence.