Production AI

The map of the Production AI skill path — what each chapter teaches, how they fit together, and where to start.

Build eval suites, catch prompt regressions, and stop shipping on vibes. Practical evaluation patterns for AI engineers.

Deploy, monitor, and operate AI systems that stay reliable at scale.

Instrument, debug, and optimize multi-step AI agents in production.

Protect AI applications from prompt injection to compliance — the security every AI engineer needs.

Master token economics, model routing, and budget governance to run AI sustainably.

Build responsive AI interfaces with SSE, streaming APIs, partial JSON parsing, and real-time tool calls.

Architect reliable, scalable AI-powered applications for production.

Design patterns engineers can implement today to build AI interfaces users actually trust.

Prepare for model deprecations, build abstraction layers, and route across providers with confidence.

Why k6 and Locust lie about streaming LLMs — and the metrics, tools, and gates that actually hold up under traffic.