Baseline Enterprise RAG, From PDF to Highlighted Answer
The article discusses the development of a minimal version of a Retrieval-Augmented Generation (RAG) system that processes PDFs to provide highlighted answers. It outlines the four key components of the pipeline, including document parsing and question parsing, which work together to produce a sourced answer. The system is designed to be simple and efficient, avoiding complex frameworks while still delivering structured JSON outputs and annotated PDFs.
- ▪The RAG system processes PDFs to return sourced answers with highlighted text.
- ▪It consists of four main components: document parsing, question parsing, retrieval, and generation.
- ▪The system is built with minimal dependencies, using libraries like pymupdf and pandas for efficient processing.
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LLM Applications Baseline Enterprise RAG, From PDF to Highlighted Answer Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. angela shi May 29, 2026 41 min read Share Photo by Curvd, via Unsplash The fastest way to understand what RAG is is to build the smallest version that actually works, run it on a real document, and look closely at what just happened. That’s this article. About a hundred lines of Python (no vector database, no framework, no agents) running on the Attention Is All You Need paper (Vaswani et al. 2017; arXiv non-exclusive distribution license, declared on the arXiv abstract page), returning a sourced answer with the exact source lines highlighted on the page.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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