I built an eval-driven financial system that treats “confident wrong answers” as the main failure mode [P]
A developer has created TrustRAG Finance, a retrieval-augmented generation (RAG) system designed for financial research that prioritizes preventing confident incorrect answers. The system employs a multi-stage pipeline including hybrid retrieval, provider-neutral LLM synthesis, and claim-level citation verification. It also features an independent groundedness judge, confidence scoring derived from system signals, and a human-in-the-loop review process, all logged on a tamper-evident audit ledger. AI
IMPACT This tool demonstrates a novel approach to RAG system design, focusing on reliability and accuracy in financial contexts, which could influence future development of specialized AI applications.