The AI Testing Framework Every Business Needs (But Few Use)
from Applied Intelligence
Keith Richman sits down with Hamel Husain, machine learning engineer and founder of Parlance Labs, to demystify AI evaluations (evals). Hamel breaks down why generic AI testing metrics fall short and how businesses can actually measure, debug, and improve their AI applications in the real world. They explore the pitfalls of simply slapping a chatbot on an existing product, the importance of iterative error analysis, and why starting simple with the most powerful models beats reaching for immediate complexity. Whether you're an executive fielding AI mandates or a developer building the stack, Hamel shares actionable advice on how to stop building the wrong things faster and start deploying AI that truly moves the needle. Chapters 00:00:00 Introduction: Why AI Testing Matters More Than You Think 00:01:52 What Are AI Evals and Why Every Business Needs Them 00:04:03 The Generic Metrics Trap: Why Off-the-Shelf Testing Fails 00:11:57 The Two Biggest Failure Modes in AI Implementation 00:13:37 Moving Fast vs Being Deliberate 00:15:04 The Slop Problem 00:21:24 Guardrails Done Right 00:23:45 Model Selection Strategy 00:27:25 Build vs Buy: When to Use Consulting vs Internal Teams 00:29:40 The Bootcamp Approach 00:31:19 The Million Lines of Code Myth 00:32:46 Embracing Mistakes and the Experimental Mindset 00:34:33 Personal Tech Stack and the OpenClaw Reality Check #ArtificialIntelligence #MachineLearning #AITesting #TechLeadership #SoftwareEngineering #DataScience #OpenAI #ProductManagement #GenerativeAI #AIEvals
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