Tip: Evaluate Before You Ship with a Simple Test Set

The single best habit you can build as an AI developer is creating an evaluation set before deploying any new model, prompt, or pipeline change. It takes an hour upfront and saves days of debugging downstream.

Start small. Twenty to fifty representative examples covering the range of inputs your system will see is enough to catch most regressions. Include edge cases: ambiguous inputs, unusual formats, the cases that broke your system before.

For each example, define what a correct output looks like. This might be an exact match, a semantic similarity threshold, a classifier that checks for required elements, or a rubric scored by another LLM. Choose the evaluation method that matches the criticality and structure of your task.

Run this evaluation every time you change a prompt, swap a model, update retrieval configuration, or modify any component that touches model inputs or outputs. Automate it in your CI pipeline if you can. Make it impossible to deploy without passing the eval.

You will catch regressions that manual testing misses. You will have data to justify changes to stakeholders. And you will develop an intuition for which changes are safe and which ones need careful validation.