Free Email Course
AI Evals for Product Development
30 emails over 6 weeks. Learn to evaluate AI features with practical patterns you can reuse. Instrumentation, measurement, metric design, experiments, and shipping decisions.
Welcome to the AI Evals for Product Development Email Course
A free, 30-day course on how to evaluate AI products in a way you can trust.
Your Evaluation Brief: pick the feature and the decision
Pick one AI feature you care about and one decision you need to make about it soon.
The Evaluation Surface Map: what are you measuring end to end
Most teams start by scoring the model output, then get confused when the product still fails.
Failure Taxonomy: turn messy traces into categories you can act on
If you can't name the pattern, you end up playing whack-a-mole with one-off bugs.
Distribution Thinking: why averages hide risk
An overall accuracy number can look great while a critical segment gets wrong answers on the queries that matter most.
Cost, Latency, Quality: pick your trade-offs on purpose
You can't improve all three at once. Every change pushes one dimension at the expense of another.
The Minimum Logging Spec: what to capture so evals are possible
You can't check anything you didn't capture.
How Little Instrumentation Is Enough?
You'll know the moment you hit a metric drop you can't explain with the fields you have.
Trace Design: make behavior reproducible
A failure you can reproduce is a failure you can fix.
Regression Suite and CI Gates: stop regressions before deploy
A regression suite in CI turns trace-level reproducibility into a deployment gate.
LLM App vs RAG vs Agent: what you must capture to diagnose failures
They fail in different places because their components are different.
Define User Value: what 'good' means outside the model
Instrumentation can tell you retrieval returned the right documents. None of that tells you whether the user accomplished what they came to do.
Ground Truth and Coverage: where your eval cases come from
Your riskiest assumption is only useful if you have eval cases that stress it.
Signal Catalog: gates vs diagnostics vs drivers
Every signal in your eval suite plays one of three roles.
Similarity and Retrieval Metrics: isolate where failure lives
Similarity metrics are fast and cheap. They are also blind in specific, predictable ways.
Rubrics and Judges: measure quality when similarity falls short
When correctness depends on judgment, not overlap, similarity metrics can't get you there.
Metric Strategy: build a suite that supports decisions
Some scores must pass before you ship. Others you track and improve over time.
Metric Patterns: pick the archetype that matches the workflow
If you measure a search-RAG feature and a drafting feature the same way, you'll optimize for the wrong behavior.
Metric Validation: prove your metric is decision-ready
Your metric might move while user value stays flat.
Segmentation: find where it works and where it breaks
Your metric can look green overall while one segment is failing badly.
Driver Analysis: explain variance and pick interventions
Knowing where performance drops is not the same as knowing why.
Evaluation Pipelines: make eval repeatable and queryable
Do results end up in a spreadsheet someone emails around, or in a system you can query six months from now?
Dataset Lifecycle: iteration sets vs test sets vs holdouts
If you iterate on the same evaluation cases long enough, your metrics will look great. Then production drops.
Experiments for Stochastic Systems: design for noise and tails
Real users are noisy. They type queries you didn't anticipate and behave differently on Mondays than Fridays.
Rollout Gates: what must be true before exposure
The experiment proved it works at 5% of traffic. Shipping to 100% means the blast radius goes from 200 users to 4,000.
Monitoring: catch silent failures without evaluating everything
Signal-based sampling catches 90% of problems at 1% of the cost.
Ship, Ramp, Hold, Roll Back: what the evidence justifies
Not a clean pass or clean fail. Conflicting evidence, where aggregate correctness holds but a critical segment is regressing.
Findings to Actions: what to change next and how you'll know
A decision without follow-through is just a meeting outcome.
Prioritization: what to fix first
Three interventions, one team, limited weeks. Which one you do first, and what evidence justifies that order.
Ownership: who owns quality, metrics, and the ship decision
Without explicit ownership, prioritized backlogs become shared to-do lists where nothing moves.
Evaluation Cadence: how this becomes a real operating system
Without a recurring rhythm, the RACI collects dust and evaluation quality degrades between incidents.
Start evaluating AI features with confidence
Free. No spam. Day 1 lands in your inbox right after you sign up.