I've spent the last few months architecting a RAG system for a regulated environment. I am not a developer by trade, but I approached this with a strict "systems engineering" and audit mindset.

While most tutorials stop at "LangChain + VectorDB", I found that making this legally defensible and operationally stable required about 40+ additional components.

We moved from a simple ingestion script to a "Multi-Lane Consensus Engine" (inspired by Six Sigma) because standard OCR/extraction was too hallucination-prone for our use case. We had to build extensive auditing, RBAC down to the document level, and a hybrid Graph+Vector retrieval to get acceptable accuracy

The current architecture includes:

Ingestion: 4 parallel extraction lanes (Vision, Layout, Text, Legal) with a Consensus Engine ("Solomon") that only indexes data confirmed by multiple sources

Retrieval: Hybrid Neo4j (Graph) + ChromaDB (Vector) with Reciprocal Rank Fusion

Performance: Semantic Caching (Redis) specifically for similar-meaning queries (40x speedup)

Security: Full RBAC, Audit Logging of every prompt/retrieval, and PII masking.

I documented the complete feature list and gap analysis

https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125...

My question to the community: Looking at this list – where is the line between "robust production engineering" and "over-engineering"?

For those working in Fintech/Medtech RAG: what critical failure modes am I still missing in this list?