Write your first eval

After this lesson you can: Measure whether a prompt change actually helped.

First: Call an LLM API from Python

Watch

• Deep Dive into LLM Evaluation with Weights & Biases - DeepLearningAI, 59 min. Directly targets LLM evaluation and is long enough to frame evals as engineering measurement.
• LLM as a Judge: Scaling AI Evaluation Strategies - IBM Technology, 6 min. Relevant focused overview of LLM-as-judge, useful for prompt evaluation despite unknown language metadata.
• How to evaluate ML models | Evaluation metrics for machine learning - AssemblyAI, 10 min. General model metrics can ground engineers in measurement thinking before applying it to prompts.

Notes

An eval is a small, repeatable test suite for an LLM behavior. Instead of changing a prompt, trying two examples, and trusting your taste, you collect representative inputs, define what good output means, run the old and new prompt on the same cases, and compare results. The goal is not to prove the prompt is perfect; it is to make improvement visible and regressions harder to miss.

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