Free course · Product Academy

Advanced: AI Evals Deep Dive

Define quality, design useful rubrics and build evaluation datasets for AI products.

3 modules · 12 lessons. Quizzes, flashcards and saved check-ins. Learn at your pace.

Evaluation during development, before release, and in production, with new failures feeding back into tests. — Define quality, test it throughout development, and learn from production failures.

What Evals Are and Why They Matter

Learn the fundamental mental models for AI product evaluation, why traditional QA doesn't work for LLMs, and how evals tie to product quality and user trust.

Why Traditional QA Fails for AI Products
The Taxonomy of Evals: Unit, Integration, Regression, and Adversarial
When to Run Evals: Development, Pre-Release, and Production Monitoring
Coverage Strategy: Happy Paths, Edge Cases, and Failure Modes

Designing Rubrics That Work

Master the art of writing concrete scoring criteria, from binary pass/fail to nuanced multi-dimensional rubrics that capture what good looks like.

Anatomy of a Good Rubric: Criteria, Scales, and Examples
Choosing Rubric Dimensions: Accuracy, Helpfulness, Safety, Tone
Binary vs. Likert vs. Custom Scales: When to Use Each
Real Rubric Examples: Customer Support, Code Generation, Summarization

Building and Running Eval Workflows

Go from theory to practice: assemble eval datasets, choose human vs. LLM-as-judge, interpret results, and integrate evals into your product development cycle.

Creating Eval Datasets: Sourcing, Labeling, and Versioning
Human Graders vs. LLM-as-Judge: Trade-offs and When to Use Each
Reading Eval Results: Aggregate Scores, Distributions, and Regressions
Communicating Evals in Interviews: Frameworks and Examples