8 modules, 34 lessons

Modules

1. 1

   Module 1: AI Evaluation Foundations

   What AI evaluation is, how models learn from human feedback, how evaluation differs from training and annotation, and the career paths available.

   4 lessonsFree
2. 2

   Module 2: AI Evaluation Assessment Fundamentals

   The core skills every AI evaluator needs: interpret prompts, follow evaluation guidelines, compare responses, apply quality standards and make objective decisions.

   5 lessons
3. 3

   Module 3: Writing Evaluation Rationales

   Explain your score in a way that survives review. Most of your time is spent writing rationales against real scenarios and comparing them to a scored standard.

   4 lessons
4. 4

   Module 4: Data Annotation Fundamentals

   Annotation is the other half of the industry and, for many people, the easier way in. In evaluation your judgement is the product; in annotation your consistency is the product.

   6 lessons
5. 5

   Module 5: Performing Well on Real Projects

   Every module until now has been about getting in. This one is about staying in: project rules, how you are measured, feedback, quality and speed.

   3 lessons
6. 6

   Module 6: How to Pass Assessments and Get Hired

   How recruitment and screening work across the major platforms, what each assessment and application flow looks like, and how to prepare for the AI video interview.

   5 lessons
7. 7

   Module 7: Red-Teaming AI Models

   Deliberately build an input that makes a model fail, then document that failure precisely enough that someone else could reproduce it.

   2 lessons
8. 8

   Module 8: Assessment Playbooks

   Platform-by-platform playbooks for the assessments that gate AI evaluation and data annotation work. Study guides, not answer keys. New playbooks are added over time.

   5 lessons