Free Course + Certification
AI Evals for Product Development
36 lessons across 6 weeks. Learn to evaluate, measure, and ship AI-powered product features with confidence. Earn weekly quiz certificates and a final certification exam. Free and open.
Start Lesson 1.1About this course: Due to the fast pace of AI, this course was retired before full release. Slides and speaker notes are complete for all 36 lessons, but exercises, datasets, and video walkthroughs referenced in some lessons are not available. If you're looking for the future of AI evals with live instruction, check out Automate AI Evals with Claude Code.
What AI evaluation is and why it requires a different approach
Why don't the evaluation methods I already know work for AI-powered product features?
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Product evaluation framework for AI systems
What is the full evaluation surface for an AI feature, from inputs to user outcomes?
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Failure surfaces and annotation-based analysis
Where do AI systems break in practice, and how do we turn failures into structured evidence?
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Distributional thinking for AI quality
How do we reason about AI behavior when outputs vary across inputs, contexts, and users?
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The cost-latency-quality frontier
How do we reason about quality improvements that change cost and latency?
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Week 1 Quiz
5 questions · Earn your weekly certificate
What to log and why it matters for AI evaluation
What evidence do I need to capture so evaluation is possible and decisions are auditable?
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How little instrumentation is enough?
When is input/output logging enough, and when do I need deeper traces?
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Trace design and reproducibility
If we need to explain or reproduce a behavior, what must be true about our trace design?
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The regression safety net — evaluation in CI/CD
How do we prevent regressions before a deploy when outputs are non-deterministic?
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How instrumentation requirements differ by system type
What must be captured for this system type so we can diagnose the failures that matter?
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Designing instrumentation that scales with the product
How do we keep observability useful at scale without drowning in data?
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Week 2 Quiz
5 questions · Earn your weekly certificate
Grounding evaluation in user value
What does good mean for this feature from the user's perspective, not the model's?
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Ground truth sources, regression suites, and synthetic data
What are we comparing against, and how do we cover the long tail?
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Deriving evaluation signals from available ground truth
Given imperfect ground truth, what signals can we compute and what are they for?
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Similarity metrics and retrieval-specific metrics
When can similarity approximate quality, and how do we isolate retrieval failures?
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Semantic metrics with human and model judges
When similarity is not enough, how do we measure whether the output helped the user?
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Scaling semantic evaluation with statistical confidence
How do we scale judgment while preserving measurement credibility?
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Week 3 Quiz
5 questions · Earn your weekly certificate
Metric strategy — blocking metrics vs optimization metrics
How do we build a metric system that supports decisions without breaking the link to user value?
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Metric design patterns for AI features
What metric archetype fits this feature and user workflow?
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Metric validation — correlation to outcomes and sensitivity to change
How do we know this metric is meaningful, not just convenient?
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Segmentation strategy for AI systems
Where does the system work well, where does it fail, and how do we structure segments to see it?
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Driver analysis — explaining variance and choosing what to change first
What is driving performance differences, and what should we change first?
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Metric specifications, thresholds, baselines, and release criteria
What does good enough to ship mean in metric terms, and how do we prevent regressions?
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Week 4 Quiz
5 questions · Earn your weekly certificate
Evaluation pipeline architecture and environments
How do we operationalize evaluation so it is repeatable, queryable, and tied to outcomes?
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Test set strategy and dataset lifecycle
How do we iterate fast without overfitting our evaluation?
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Experiment design for stochastic systems
How do we run online tests when outcomes are noisy, long-tailed, and mediated through user behavior?
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Launch readiness and rollout gates
What must be true before exposure, and what do we do if it degrades?
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Monitoring for drift and regressions
How do we detect silent failures in production without evaluating everything?
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Building evaluation automation end-to-end
How do we connect offline evaluation, CI/CD checks, and production monitoring into one system?
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Capstone lab — evaluation pipeline build
Can you build a working evaluation pipeline that runs sampling, judging, aggregation, and reporting?
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Week 5 Quiz
5 questions · Earn your weekly certificate
Decision-making under uncertainty
Given conflicting evidence, what decision is justified?
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Translating evaluation signals to product actions
Given what we observed, what should we change next, and how will we know it helped?
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Prioritization and iteration using evaluation evidence
What should we fix first, and how do we learn fast?
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Ownership model — the AI Reliability Lead
Who owns the data, the metric, and the ship decision, and how do we prevent ambiguity?
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Evaluation cadence and governance
How do we run evaluation as a system that stays tied to development and production?
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Communicating AI product impact
How do I report impact in a way that earns trust and drives alignment?
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Week 6 Quiz
5 questions · Earn your weekly certificate
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