RAG is everyone’s favorite GenAI trick, but running it in production is where the scars show. In this talk, I’ll share my personal journey of scaling Retrieval-Augmented Generation inside enterprises... where “good enough retrieval” often breaks under freshness requirements, attribution demands, and compliance audits.

We’ll unpack the messy but crucial details: choosing embeddings that don’t silently drop context, tracing answers back to their sources (provenance), tuning retrieval freshness vs. latency (TTR), and the tradeoffs between recall and precision. I’ll also showcase two techniques I’ve published about recently: dynamic semantic chunking (to avoid context dilution) and adaptive-k retrieval (a smarter way to balance recall vs precision without extra latency).

The session won’t be a slide monologue. Together with the audience, we’ll stress-test a live RAG system and watch how tweaks in chunking, embeddings, or retrieval settings alter both performance and trust. The big takeaway: RAG isn’t about bolting a vector DB to an LLM, it’s about engineering provenance-aware retrieval pipelines that survive the real world.