Anyone can call an LLM — that's the commodity. Turning a flaky generator into a system you can ship is review, QA and security work you'd never run by hand. This course line teaches you to run it solo — as code.
The thesis: an LLM is a commodity you'll swap in a month; the judges you build around it — grading every output against your bar and gating the release when it falls short — are what's yours. You set the standard; the harness enforces it across thousands of outputs, the review, QA and eval coverage one person could never run by hand. LLM is the commodity; the harness is the moat.
Course hero under final review
AI-generated fictional presenter
Every track is the same spine applied to one domain. Learn it once in the anchor course, then watch it secure an app, gate a release, govern a fleet of coding agents, or grow an eval set that compounds.
Three things no marketplace course bundles together.
Every claim traces to published, public research — OWASP LLM & Agentic Top 10, MITRE ATLAS, RAGAS, MT-Bench, DORA, NIST AI 600-1, C2PA — cited and credited, never hand-waved.
The 58 lessons are paired with 57 Python labs that run offline, stdlib-only, with the same output every time. Not slides — code you run, then point at your own system.
Every metric is illustrative and labelled. We teach you to measure your own numbers — never "our system hits X%." The honesty is the product.
Start with the anchor. Add the track that matches your work — or take the whole harness.
The domain-agnostic spine: discover failures, write judges as code, validate them against a compact labeled set, gate, retry, calibrate, wire into CI.
Traces-first (OpenTelemetry), golden datasets, the RAG triad reference-free, readiness scoring & the Pareto frontier, reproducible CI gates, the production loop.
Claude Code in production: AGENTS.md as contract, specialized subagents, hardening & determinism, red-team your own agents, judge-driven review — governance, not a feature tour.
OWASP LLM & Agentic Top 10, MITRE ATLAS, prompt-injection taxonomy, red-team as a repeatable harness, detection-as-code → SIEM, compliance derived.
Validate your own product: run it as the eval, orthogonal quality dimensions, calibrated gates, the living question bank, and dogfooding wired into CI — ship on evidence, not "looks fine."
Goal-directed NPC simulation, multi-turn exploration, and the honest breadth/mass validity boundary: personas generate the space, not the calibration. The limit nobody else teaches.
The eval set is the asset, not the model: curate a seed bank, grow it from real failures, stratify & cover, dedup, judge-label with κ agreement, version and catch regressions — the bank that compounds.
Quality isn't free: the three-axis gate, latency percentiles over means, the cost/quality Pareto frontier, route-cheap-escalate-hard, semantic caching, and budget gates that fail regressions in CI.
Core method → Complete bundle → Full Harness. Review what each option includes now; purchase links and final pricing appear only after launch approval.
New here? The Level-0 primer is free — read it while launch details are finalized.
Want the toolkit, not the course? Pair any track with the AI Coding OS pack → (18 install-ready skills) — the harness, pre-built.
.zip with an index.html launcher: open it in any browser,
read on any device, run the labs on a desktop. No account, nothing calls home. Updates arrive through your
store library.