Evaluation
Evaluation datasets
Build repeatable cases that represent real agent work.
Lesson overview
Evaluation datasets
Start with real tasks and include normal, edge, failure, and adversarial cases. Store the expected outcome, important constraints, and the reason the case matters.
Turn production incidents into regression cases.
Learning path
Theory → Example → Code → Practice → Quiz → Challenge → Completion
Step 1
Theory
Evaluation datasets
Start with real tasks and include normal, edge, failure, and adversarial cases. Store the expected outcome, important constraints, and the reason the case matters.
Turn production incidents into regression cases.
Step 2
Example
Example
Apply Evaluation datasets to a realistic production scenario and trace the decision step by step.
Step 3
Code
Code
Add implementation notes or a runnable example for this concept.
Step 4
Practice
Practice
Write down the inputs, expected output, constraints, and one failure case for Evaluation datasets.
Step 5
Quiz
Quiz coming soon.
Step 6
Challenge
Challenge
Design a production-ready solution for Evaluation datasets and explain one important trade-off.
Complete every stage
Work through every step in order, then the lesson will be marked complete.
Each chapter and subtopic has its own public URL under /ai-agent.