Evaluate AI systems

Why this skill matters

A good demo does not establish reliability on unfamiliar inputs.

What to learn

• Build a representative test set
• Choose metrics for the cost of errors
• Compare versions against a baseline

Before you begin

Basic statistics and test design

PUT IT INTO PRACTICE

Build a work sample

Create a small labeled classification dataset. Compare two approaches and explain false positives and false negatives.

Evidence of progress

An evaluation report showing failure examples and a justified acceptance threshold.

This exercise is an original ToolGrail suggestion. Use public, synthetic, or approved data.