Evaluate LLM Outputs Like an Engineer
Build a small but real evaluation suite for an LLM-powered feature: define success criteria, write graded test cases, and implement code-based and LLM-as-judge grading, following Anthropic's documented evaluation methodology.
Prerequisites: completion of Prompt Engineering Fundamentals, Python, and an Anthropic API key.
An evaluation, or "eval," is a test for an AI system: give it an input, then apply grading logic to its output to measure success. This is the discipline that separates "I tried a prompt and it seemed fine" from being able to say, with evidence, whether a change actually improved your system — and it's the step most people skip until it becomes a bottleneck.
You'll build an eval suite for the feedback-categorization task from the Prompt Engineering Fundamentals project (or any LLM-powered task you've already built), covering three grading approaches Anthropic's documentation recommends choosing between based on speed, reliability, and how much nuance the task requires: code-based grading, human grading, and LLM-based grading.
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