Everyone ships AI. Nobody measures it.
In 30 minutes you'll build an eval system — the skill that separates AI hobbyists from professionals.
It starts with ranking five responses.
You built an AI feature. Your boss asks "how good is it?" You say "it seems pretty good."
That's not an answer.
Below are 5 AI responses to the question: "How do I make pasta?"
Click to rank them from best (1) to worst (5):
Different tasks need different metrics. The metric you choose shapes the product you build.
Accuracy, precision, recall, F1 for classification. BLEU/ROUGE for text generation. Human preference for open-ended tasks.
You're building a spam classifier. Drag the predictions into the right boxes:
Your eval is only as good as your test data. A good test set is the most valuable thing in your AI project.
Build a test set: input → expected output pairs. Include edge cases, adversarial examples, and typical queries.
Create 10 test cases for a customer support bot:
Running evals by hand doesn't scale. Use an LLM to judge another LLM's outputs (LLM-as-judge).
LLM judges agree with humans 80% of the time — enough to catch regressions.
Write an evaluation prompt:
Change something (prompt, model, temperature) → run eval → compare.
Every improvement to your AI should start and end with an eval.
Test two versions of your system prompt:
Track your metrics over time:
You can now measure, compare, and improve any AI system.
This is the skill that separates professionals from hobbyists.