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How to Test Your AI Like a Pro (Even If You’re Just Starting)
Building AI is one thing. Proving it works reliably is another. This beginner’s guide shows you how to actually test your AI so it does not fall apart in the real world.
Theo T
August 27, 2025
🔍 The Big Idea
Shiny demos are fun, but without proper testing they can be flaky. Peter Yang’s guide walks through the four key types of AI evaluations: programmatic, human, LLM judge, and user tests. Using the example of a customer support agent for ON running shoes, he explains how to move from “looks cool” to “works in production.”
🧩 How It Works / What Happened
Programmatic evals – Automated checks for basic issues
• Example: Make sure answers reference the “30-day return” policy, avoid mentioning competitors, and keep responses within the right length.Human evals – Experts label a golden dataset
• Real reviewers decide what “good” looks like so you can measure new answers against it.LLM judge evals – Use AI to scale evaluations
• Feed the golden dataset to an LLM and let it score large batches of answers quickly.User evals – Feedback from real customers
• Track whether responses are actually helpful, accurate, and aligned with user expectations.
💡 Why It Matters
Eval Type | Why It Helps | Example Benefit |
|---|---|---|
Programmatic | Catches obvious errors | Prevents wrong policy info or odd lengths |
Human | Sets the quality benchmark | Engineers know exactly what “good” means |
LLM Judge | Expands evaluation at scale | Flags hundreds of weak answers quickly |
User | Captures real-world value | Shows where AI helps or frustrates people |
💪 Try This Today
Make a simple eval plan:
• Pick one question your AI answers often, like “What is your return policy?”
• Write a programmatic rule that checks if the reply mentions “30-day return.”
• Have a colleague label one example of a strong answer and one weak answer.
• Ask an LLM to rate a batch of your AI’s answers against those labels.
• Share one answer with a real user and get feedback on clarity and usefulness.
🧭 Bottom Line
AI without evaluations is like flying without a pre-flight check. If you want users to trust your product, you need clear tests that catch errors, track progress, and measure impact.
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