How to Build a PDF RAG Pipeline: Retrieve, Rerank, and Summarize Efficiently

A practical runbook for building a retrieval-augmented generation (RAG) pipeline explains how to summarize only the most relevant passages from large PDF documents rather than feeding entire files to a language model. The approach involves extracting and chunking PDF text, embedding chunks for semantic search, retrieving a broad candidate set, and then reranking it to a smaller, precise subset before generating a summary. The author emphasizes tracking page provenance, chunk IDs, and character offsets so that every output sentence can be traced back to its source passage. Three key quality signals are monitored: relevant-page recall before reranking, useful evidence in the final top set, and unsupported statements in the generated summary. The runbook also warns that poor PDF extraction quality — such as broken reading order or missing tables — is an upstream dependency that can undermine the entire pipeline before any model is involved.