Chain of Thought Mindset Over Metrics: How to Approach AI Engineering | Hamel Husain
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Aug 20, 2025 Hamel Husain, an independent AI consultant with a rich history at Airbnb and GitHub, dives into the mindset shift required for successful AI engineering. He critiques the reliance on vanity metrics, arguing they lead to misconceptions about AI performance. Instead, he champions custom evaluations and error analysis as the backbone of robust AI products. The discussion also highlights the importance of domain expertise in refining AI metrics and encourages an experimentation mindset to foster continuous improvement and reliability in AI systems.
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Design Process Before Picking Tools
- Avoid asking only 'what tools do I use' and instead design the right evaluation process.
- Hamel advises teams to define a disciplined eval process before choosing tooling.
Vanity Metrics Create False Security
- Generic dashboard metrics often create an illusion of safety without catching real failures.
- Hamel warns that vanity metrics can waste time and mislead teams about true system reliability.
Custom Evals For Domain Risks
- Eval design must be customized to your app's domain risks and evolving architecture.
- Atindriyo emphasizes listing workflow pains and authoring targeted evals that evolve with the app.