How to Test AI Models: The 2 Methods That Actually Work

Stop vibe checking your AI. In this Python tutorial, you'll master the two foundational pillars of objective AI evaluation: Functional Correctness and Semantic Similarity. You will learn: ✅ How to build unit test evaluators for AI-generated code, SQL, or JSON ✅ Why simple "exact match" tests fail in production (The "Grandma Problem") ✅ How to measure what your AI means, not just what it says (embeddings + cosine similarity) ✅ A simple decision framework for choosing the right test for any task ✅ Practical Python code you can adapt for your own projects This is video #2 in our AI Evaluation Masterclass series: - Video 1: The AI Evaluation Crisis - Video 2: Two Testing Methods ← YOU ARE HERE - Video 3: Building an AI Judge (Coming Next!) Chapter Timestamps 00:00 The Two Ways Every AI App Fails in Production 00:48 Pillar 1: Functional Correctness (When Code Must Work) 01:48 Python Demo: Building a Unit Test Evaluator 02:58 Why Exact Tests Fail (The "Grandma Problem") 03:57 Pillar 2: Semantic Similarity (When Meaning Matters) 04:02 What Are Embeddings? (Converting Words to Numbers) 05:06 Python Demo: Measuring Meaning with Cosine Similarity 06:28 The Decision Framework: Which Test When? 07:31 Your Testing Toolkit & Next Video Preview 💬 What's your biggest AI testing challenge right now? Drop a comment! 🔔 **Subscribe for practical AI insights** - we're breaking down how modern AI actually works, one video at a time. This presentation is inspired by the core concepts in the book "AI Engineering" by Chip Huyen. If you want a deeper dive into these topics, I highly recommend checking it out. 🎓 Join our FREE AI Engineering Community on Discord: https://discord.gg/KpnJQbgpjt #AIEvaluation #PythonTutorial #LLMTesting