# Apify blog post walks through building a PDF question-answering pipeline with Python and RAG

> Apify published a tutorial on constructing a Python pipeline that uses retrieval-augmented generation to answer questions from PDFs and cite the source page.

- Canonical: https://extractfeed.io/story/apify-blog-post-walks-through-building-a-pdf-question-answer-decbc11/
- JSON: https://extractfeed.io/api/v1/stories/apify-blog-post-walks-through-building-a-pdf-question-answer-decbc11.json
- Beat: Extraction & Parsing · Evidence: Primary source · Type/significance: analysis/2 · First seen: 2026-10-15T00:02:22.767741+00:00 · Updated: 2026-10-15T00:02:22.767741+00:00 · Edition: 2026-11-05
- Framing: model-written (headline, standfirst, why it matters, tags); source facts deterministic

## Briefing
- Apify: Apify published a tutorial on constructing a Python pipeline that uses retrieval-augmented generation to answer questions from PDFs and cite the source page.

## Why it matters
This tutorial reflects the growing trend of combining extraction (PDF parsing) with RAG for grounded, verifiable answers. By emphasizing citation checking, it highlights a key challenge in AI-assisted data extraction: trustworthiness of retrieved content. Practitioners can use this as a template for building auditable QA systems over documents.

## Sources
- Primary source · Apify · 2026-10-14 — [How to do question answering from a PDF with Python and RAG](https://blog.apify.com/how-to-do-question-answering-from-a-pdf/)

## Watch next
How will Apify's tooling evolve to support real-time citation verification in RAG pipelines?

Topics: Apify, pdf-extraction, rag, question-answering, python, citations, tutorial

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