~/courses/ai-product-engineering
AI Product Engineering
Take an AI feature from "prompt in a playground" to a shipped, monitored, cost-bounded production surface — correct, observable, and secure-by-default.
// The loop
spec the behavior + eval → build the smallest correct version → wire output/streaming/tools → add the eval harness → add tracing + cost controls → gate cloud egress → ship → monitor → write the playbook
// The 6-phase roadmap
- 01 LLM app fundamentals & prompt design
- 02 Provider integration & streaming
- 03 RAG — retrieval, grounding & citations
- 04 Agents & tools (MCP, multi-step, HITL)
- 05 Evals, observability & cost
- 06 Ship to production (secure-by-default)
The construction counterpart to the AI-security course. Where that one teaches you to break and harden agents, this teaches you to build production AI apps well — correct, observable, cost-controlled, and secure by default.
Every feature follows the same rhythm: spec the behavior and the eval before the code, build the smallest correct version, then layer in structured output, tools, evals, tracing, and cost controls. Security isn’t a final-phase bolt-on — any feature touching sensitive data goes through an assert → redact → log egress discipline from day one. The deliverable is a shipped, monitored, cost-bounded surface.