What this video covers
This 32-minute recorded workshop shows the retrieval architecture Rockmere engineers deploy when a client needs enterprise RAG to survive production in a regulated environment. One whiteboard, three real production examples, and the decisions that separate a demo from a system an auditor will sign off on. The core argument: in production RAG, retrieval is the system and the LLM is the renderer.
Why do enterprise RAG implementations fail in production?
Most enterprise RAG pilots fail in production because the retrieval layer returns the wrong context, not because the language model is weak. Naive vector-only RAG returns irrelevant or incomplete passages on roughly 40 percent of real enterprise queries, and a strong model on bad context still produces a confident wrong answer. The session walks the full retrieval path, from query intake to reranked context delivery, and shows where each failure mode hides.
The fix is layered, and the video builds it step by step:
- Hybrid search. Combine dense vector search with sparse keyword (BM25) search so exact terms, codes, and acronyms are not lost in the embedding.
- Cross-encoder reranking. Re-score the top candidates with a cross-encoder before they reach the model, which lifts the passage that actually answers the query to the top.
- Chunking and metadata. Size chunks to the document type and attach metadata so retrieval can filter by source, date, and permission.
How do you evaluate a RAG system?
You evaluate a RAG system with a measurement harness wired in during week one, not bolted on after launch. The workshop demonstrates the RAGAS evaluation harness scoring faithfulness, answer relevancy, and context precision on every change, so a tuning decision is a number on a dashboard rather than an opinion in a meeting. Without that harness, teams cannot tell whether a change to chunking or reranking helped or hurt.
How is governance built into the retrieval layer?
Governance lives in the retrieval layer, not in a policy document written afterward. The session walks through the pattern Rockmere ships into regulated environments:
- Access-control filters at query time, so a user only retrieves documents they are cleared to see.
- Audit logging of every query, retrieved source, and generated answer, so any output can be traced back to its evidence.
- A documentation package that model risk teams reviewing under SR 11-7 will accept.
Who is this video for?
This video is for AI architects, applied AI leaders, and engineering managers running RAG in regulated environments. If your pilot demoed well and then stalled before production, the 32 minutes will show you which retrieval decisions to make first and likely save weeks of rework.
Want to pressure-test your own architecture?
Talk to a RAG engineer. We run 30-minute architecture reviews at no cost and walk your retrieval design against the patterns in this session. The audit-ready handover takes longer, and we will scope that honestly.