AI Skills
Build AI products that survive production
A curated track through the skills for shipping AI features that hold up under real use: specs, evals, agents, RAG, guardrails, testing, and observability. Each one names the hard part a generalist misses, not just the steps.
Two learning paths
Start here if you want a sequence rather than a menu. Same skill library, ordered for the job you actually have.
For engineers
Spec → Prototype → Eval → Deploy → Monitor
For product managers
Market → Adoption → Health → Cost → Governance
The skills, by phase
The full AI-building set, grouped by where it sits in the lifecycle. Click any skill for the full process.
Spec & plan
Decide why AI, which model, and what good looks like before any code.
Build
Get it working: prototype, the agent loop, and grounding in your own data.
/ai-prototype-guideStand up a working version fast with AI coding tools.
/ai-agent-designMulti-step agents: tool surface, loop and termination, state, failure recovery.
/rag-implementationGround the model in your documents: RAG vs fine-tune vs long context, chunking, retrieval evals.
Evaluate & harden
Define quality, test the system, and defend it against misuse.
/ai-eval-designRubric, golden dataset, pass bar, and hard-fails. What good means, made measurable.
/ai-testing-strategyThe testing pyramid, regression suites, red-teaming, and deploy gates.
/ai-guardrails-designLayered input validation, output filtering, and abuse detection.
Ship & monitor
Watch quality, cost, and risk in production, because none of them throw an error.
/llm-observability-planQuality metrics, logging, cost-reduction playbook, drift detection, A/B in prod.
/agent-eval-harnessOffline regression suites, trajectory scoring, and production sampling. Agent quality watched live, not just pre-ship.
/ai-risk-registerA living register across accuracy, bias, security, cost, and compliance, maintained after launch.
Assess & govern
Diagnose AI readiness and set the limits before scale forces them.
/ai-adoption-evaluatorAssess whether and how AI actually gets used.
/ai-health-checkRun an AI Health Indicator assessment.
/ai-maturity-assessIndividual AI maturity self-assessment.
/ai-maturity-orgOrganizational AI maturity assessment.
Related reading
The thinking behind the skills: how to know your AI actually works, and the terms worth getting straight.
Start with the job, not the model. The three-layer framework.
The plain-English definition, and the three parts every eval is made of.
A practical guide to building an eval that catches real regressions.
Make evals part of the build loop, not a one-time check.
The failure mode evals catch most often, and how to test for it.
Plain-English definitions for evals, AI adoption, and fractional roles.
Looking for something else?
These are the AI-building skills. The full library covers product management, engineering, design, facilitation, and research too.
Browse all skills