Dear subscribers,

Today, I want to share a new episode with Shreya and Hamel.

Shreya and Hamel teach the industry-leading AI evals course taken by 4,500+ students from OpenAI, Google, and more. I asked them to do a live audit of the evals that I built for my creator skills. They then walked through how anyone can use their free Error Discovery skill in Claude Code to turn feedback into useful evals. If you want a practical, up-to-date primer on AI evaluations, then this episode is for you.

Watch now on YouTube, Apple, and Spotify.

P.S., I asked Shreya and Hamel to give 25% off the next cohort of their AI evals course that’s starting on 9/5. It has 4.7 stars with 900 reviews, which is almost unheard of on Maven. Check it out and get your company to expense it if you can!

Get 25% Off the Course Now

Shreya, Hamel, and I talked about:

(00:00) Why AI evals are completely different now with the latest models
(02:30) Demo: Live audit of the evals that I built for my AI skills
(05:14) Top-down vs. bottom-up evals and why AI sucks at the latter
(08:16) Spinning up AI agents to grade each eval criterion
(14:32) Demo: Using Shreya’s AI skill to run evals in Claude
(23:46) How to review output labels to identify failures
(34:37) How to turn failures into reusable evals
(43:19) Where human judgment is still needed

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Top 10 takeaways I learned from this episode

Live audit of the evals for my AI skill

There are two types of evals: top-down and bottom-up. When I asked Shreya and Hamel to audit the evals that I built for my podcast production skill, they found strong top-down evals, but almost no bottom-up evals.
1. Top-down evals are rules that you define upfront. Mine include “Is every takeaway 240-330 characters?” and “Does each give plain, useful advice?”
2. Bottom-up evals are failures found by comparing AI’s output with my final edits. With these evals, it’s important not to overfit to a few examples.
Compare 10–20 past examples before turning a failure into an eval. Hamel and Shreya encouraged me to run my podcast production skill on at least a dozen past interviews to compare AI’s output with my final edits. They shared a free Error Discovery skill that Claude Code and Codex can use to define evals based on this data.

Build useful evals using this free skill in 5 steps

Shreya and Hamel built a free Error Discovery skill that anyone can run in Claude Code or Codex to define useful evals based on your data. The skill walks you through this process:

Below is a link to the skill and a walkthrough of each step:

How to Build Better AI Evals with Claude Code…

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