Building explainable GraphRAG for a financial advisor use case, sharing what I learned plus the workshop that goes deep on it

Been working through a specific problem: RAG systems that use GPT models can retrieve a relevant chunk and generate a confident answer, but there's no way to show why it answered that way, no reasoning chain, just a simi