Course catalog
Measure, trace, and trust your AI like a test suite.
In 2026, prompt engineering without evals is considered junior work — evals are the line where AI development becomes engineering. Starting from error analysis and golden datasets, you'll learn to write evaluators (exact and fuzzy match, rubric scoring, and LLM-as-judge) and gate regressions in CI. Then you'll read a trace to find the slow or costly step, understand spans and metrics, and debug failures from observability data. You'll finish on the economics: token cost, caching and model routing, cutting latency, and shipping only when the eval dashboard is green.
Section 1
Stop eyeballing; start measuring.
Section 2
Match, rubric, judge, and gate.
Build and run a real AI product: it searches your own content, uses only the tools you approved, is tested the same way every time, has a plan for when it fails, and shows its speed and cost.
11 course sequence
Ship a secure TypeScript app built with AI in your workflow: tested algorithms, tools kept inside safe limits, architecture choices you can explain, and releases you can check after they go out.
16 course sequence
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Get startedSection 3
See what your AI actually did.
Section 4
Ship fast, cheap, and confident.