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