Become an AI evaluation engineer
Prove whether an AI system is good enough, and keep checking after launch.
For: Engineers and testers who want to measure and improve the quality of AI output.
- 10 steps
- stops on the route
- 10 lessons
- you finish in total
- 1 course
- the lessons come from
- ~3h reading
- all lessons together
Finish every lesson to earn a certificate of completion that anyone can verify. Jump to the certificate ↓
Honest note: The ten steps follow the AI Evaluation Engineer course. Extra reading from other courses is listed at the end. It does not count towards the certificate.
How to read this page. A roadmap is a route made of 10 steps. Each step has one lesson. There are 10 lessons in total. Extra reading is listed at the end of the page. It does not count towards your progress.
- AI Evaluation Engineer0/10 done
Certificate of completion
Earn your AI evaluation engineer certificate
Verified edition
Mark every lesson in this roadmap 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.
- 1
Step 1 of 10
Understand the role and the loop
Explain why AI needs its own testing, and what an evaluation engineer does.
Before you move on: Read 50 real AI outputs, mark them, and group the bad ones into types.
- 2
Step 2 of 10
Define what good means
Turn a vague wish into a rubric that two reviewers can apply the same way.
Lessons0/1 doneBefore you move on: Write a rubric with three dimensions and two must-never-happen rules, and test it with a friend.
- 3
Step 3 of 10
Build a test set
Collect real, varied cases with right answers, and keep a hidden set.
Lessons0/1 doneBefore you move on: Build 30 cases across five groups, split into development and hidden sets.
- 4
Step 4 of 10
Add automatic checks
Use code to check shape, matches and search quality.
Lessons0/1 doneBefore you move on: Write five code checks and work out precision and recall for a small example.
- 5
Step 5 of 10
Use judges and human review
Score open qualities, and check every judge against people.
Lessons0/1 doneBefore you move on: Compare a model judge with your own scores on 20 outputs and fix one pattern.
- 6
Step 6 of 10
Test retrieval systems and agents
Score each step of a pipeline and read agent traces.
Lessons0/1 doneBefore you move on: For ten questions, mark which step failed first and count failures by step.
- 7
Step 7 of 10
Study the failures
Find root causes and fix one thing at a time.
Lessons0/1 doneBefore you move on: Run an error analysis on 30 outputs and write one targeted fix with a before and after score.
- 8
Step 8 of 10
Gate every release
Stop changes that make quality worse, and roll out safely.
Lessons0/1 doneBefore you move on: Write a release gate with five rules and a rollout plan with a rollback step.
- 9
Step 9 of 10
Watch it in production
Sample live traffic, catch drift and feed failures back into the test set.
Before you move on: Write a monitoring plan with two alerts and a weekly ten-conversation review.
- 10
Step 10 of 10
Get interview-ready
Answer evaluation design questions with a method, and show real artifacts.
Before you move on: Answer the legal-contract summarizer question aloud in ten minutes, and fix your weakest part.
Hands-on lab · build confidence
Evaluate a help-center assistant and gate a release
Write a rubric, build a test set, score an assistant, study the failures and decide if it can ship.
No code · 4 to 6 hours
Optional
Want to go further?
These lessons come from other courses. They are extra reading. They do not count towards your progress or the certificate.
- ○Evaluating & Debugging AgentsCourse: Agentic AI Engineer
- ○Evaluation, Testing & Red TeamingCourse: AI Engineering & Agentic AI Architect
- ○Observability & TracingCourse: Agent Ops & Governance
- ○Evaluation in ProductionCourse: Agent Ops & Governance
- ○Change Management & ReleasesCourse: Agent Ops & Governance
- ○Cost, Latency & EvaluationCourse: Multi-Agent Orchestrator
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