AI Evaluation Engineer
Prove whether an AI system is good enough, and keep checking after launch.
Define quality with rubrics, build test sets, use code checks and model judges, evaluate RAG and agents, analyze errors, set release gates and monitor live quality. Ends with interview practice.
10 lessons · about 3 hours
Certificate of completion
Earn your AI Evaluation Engineer certificate
Verified edition
Mark every lesson in this course complete. You have finished 0 of 10. 10 to go.
See a sample certificate. Anyone can check a certificate with its code at /verify. It records course completion. It is not an exam or an accredited qualification.
Why AI needs its own kind of testing, what the role covers and how the evaluation loop works.
Turn a vague wish into measurable criteria with levels, examples and must-never-happen rules.
Where cases come from, how many you need, how to label them and how to avoid leaks.
Format checks, matching, precision and recall, and search measures such as recall at k.
When to use people or model judges, how judges go wrong and how to calibrate them.
Score each step of a retrieval pipeline and each part of an agent run, and read traces.
Read failures, name and count them, find root causes and fix one thing at a time.
Test every change, set thresholds and roll out with shadow runs, canaries and rollbacks.
Sample live traffic, watch for drift and turn real failures into new tests.
Common questions with strong answers, a design method and a portfolio that proves your skill.