The voice-over in this course is synthesized with a text-to-speech model from scripts written and reviewed by the instructor, and the on-screen material (notebooks, code, slides) is the instructor’s own work. A tutorial corpus never taught anyone what production feels like. This course runs GraphRAG on real public documents — US congressional records, bills and committee reports about artificial intelligence legislation, all public domain — and answers the question that decides whether you ship it: is this graph actually any good?
Section 1 is the document work nobody demos: downloading from govinfo, parsing PDFs and HTML, headers and reprinted text, a token diet that cut our corpus by a quarter, and a cost estimate before you spend anything. Then a production index, with settings, entity types and prompt tuning, and its real wall time and bill. Section 2 evaluates it: graph-structure metrics, entity-extraction quality judged by an LLM against the source text, automatically generated evaluation questions, and answer quality for local search, global search and a plain vector baseline. Section 3 builds multi-source graphs with provenance so an answer can be traced back to the source type it came from. Section 4 puts a function-calling agent in front of the graph and compares it with a simple router.
