Your proof-of-concept wowed the room. Then it hit evals, cost, governance, and latency — and stalled. This cohort is about the part nobody teaches: crossing the gap from a demo that impresses to a system that ships and survives.
There are thousands of courses for building your first RAG app. There are almost none for the two hardest questions in enterprise AI: is it actually good, and will it survive contact with production?
Most instructors have never sat in a governance review or explained a token bill to a CFO. The distance between a demo and a deployed system is where projects die — and it's exactly where two decades of shipping enterprise infrastructure is worth more than another framework tutorial.
Move from "the demo looked right" to measured quality. Build an eval harness for your own use case: golden sets, LLM-as-judge, regression gates, and the metrics that actually predict user trust.
The two conversations that kill projects. Token economics and cost controls, plus the security, privacy, and data-governance posture that gets you through enterprise review instead of stuck in it.
Where vector search hits its ceiling and structure has to carry the answer. Knowledge graphs, GraphRAG, and semantic retrieval patterns for the questions flat RAG gets wrong.
Agentic patterns and MCP without the hand-waving: tool use, orchestration, and the guardrails that keep autonomy safe. Then wire the whole thing into a deployable, observable path to prod.
I've spent 20+ years in enterprise cloud and infrastructure, and the last stretch hands-on with production GenAI: RAG, GraphRAG and knowledge graphs, agentic and MCP-based systems. I teach the POC-to-production crossing because I've had to make it — under real cost, security, and reliability constraints, with real stakeholders in the room. This cohort is that experience, distilled.
No — and that's deliberate. If you've never built with GenAI, you'll get more from a free intro course first. This cohort starts where those end: with something that already works in a notebook and needs to survive production.
Sessions are recorded, but this is a live cohort by design — the value is in working through your own use case with the group in real time. If you can't join most sessions live, wait for a future run.
Yes. The deposit holds your seat and comes straight off your tuition if you enrol. If the cohort isn't right for you, ask and it's returned — no reason needed.
The patterns are portable across providers. Hands-on work uses a mainstream managed GenAI stack; principles for evals, cost, governance, GraphRAG, and agents transfer wherever you build.