Baseline Enterprise RAG, From PDF to Highlighted Answer

A minimal Retrieval-Augmented Generation (RAG) pipeline can be built from scratch to provide verifiable answers from documents. The process is broken down into four core components: document parsing, question parsing, retrieval, and generation, using Python libraries like pymupdf, pandas, and the OpenAI API. Using the 'Attention Is All You Need' paper as an example, this approach turns a PDF into structured data to find relevant context for a user's question. The final output is a generated answer with direct citations and highlighted evidence in the source PDF, demonstrating a practical method for building grounded AI systems without complex frameworks.