When I was building security auditing tools like Git Secret Scanner, the rules were binary: a vulnerability exists, or it doesn't. But when you start building Generative AI pipelines for institutional finance, things get dangerously blurry.
Almost every RAG tutorial online shows you how to chunk a PDF, throw it into a vector database, and build a chatbot. That works fine for toy applications. But in an enterprise banking environment, a single hallucinated decimal point or a swapped currency symbol isn't just a bug—it’s a regulatory compliance violation.
Standard Retrieval-Augmented Generation (RAG) relies on dense vector search, which maps text based on semantic meaning. The problem? "Q2 Revenue was $40M" and "Q3 Revenue was $40M" are semantically identical to a vector database, but completely different to a financial auditor.
I needed a way to force language models to be mathematically deterministic. So, I built FinGuard-RAG.
The Problem: Silent Hallucinations
