Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course)

Hamel Husain and Shreya Shankar teach the world’s most popular course on AI evals and have trained over 2,000 PMs and engineers (including many teams at OpenAI and Anthropic). In this conversation, they demystify the process of developing effective evals, walk through real examples, and share practical techniques that’ll help you improve your AI product.

What you’ll learn:

1. WTF evals are

2. Why they’ve become the most important new skill for AI product builders

3. A step-by-step walkthrough of how to create an effective eval

4. A deep dive into error analysis, open coding, and axial coding

5. Code-based evals vs. LLM-as-judge

6. The most common pitfalls and how to avoid them

7. Practical tips for implementing evals with minimal time investment (30 minutes per week after initial setup)

8. Insight into the debate between “vibes” and systematic evals

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Brought to you by:

Fin—The #1 AI agent for customer service

Dscout—The UX platform to capture insights at every stage: from ideation to production

Mercury—The art of simplified finances

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Where to find Shreya Shankar

• X: https://x.com/sh_reya

• LinkedIn: https://www.linkedin.com/in/shrshnk/

• Website: https://www.sh-reya.com/

• Maven course: https://bit.ly/4myp27m

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Where to find Hamel Husain

• X: https://x.com/HamelHusain

• LinkedIn: https://www.linkedin.com/in/hamelhusain/

• Website: https://hamel.dev/

• Maven course: https://bit.ly/4myp27m

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In this episode, we cover:

(00:00) Introduction to Hamel and Shreya

(04:57) What are evals?

(09:56) Demo: Examining real traces from a property management AI assistant

(16:51) Writing notes on errors

(23:54) Why LLMs can’t replace humans in the initial error analysis

(25:16) The concept of a “benevolent dictator” in the eval process

(28:07) Theoretical saturation: when to stop

(31:39) Using axial codes to help categorize and synthesize error notes

(44:39) The results

(46:06) Building an LLM-as-judge to evaluate specific failure modes

(48:31) The difference between code-based evals and LLM-as-judge

(52:10) Example: LLM-as-judge

(54:45) Testing your LLM judge against human judgment

(01:00:51) Why evals are the new PRDs for AI products

(01:05:09) How many evals you actually need

(01:07:41) What comes after evals

(01:09:57) The great evals debate

(1:15:15) Why dogfooding isn’t enough for most AI products

(01:18:23) OpenAI’s Statsig acquisition

(1:23:02) The Claude Code controversy and the importance of context

(01:24:13) Common misconceptions around evals

(1:22:28) Tips and tricks for implementing evals effectively

(1:30:37) The time investment

(1:33:38) Overview of their comprehensive evals course

(1:37:57) Lightning round and final thoughts

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LLM Log Open Codes Analysis Prompt:

Please analyze the following CSV file. There is a metadata field which has an nested field called z_note that contains open codes for analysis of LLM logs that we are conducting. Please extract all of the different open codes. From the _note field, propose 5-6 categories that we can create axial codes from.

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Referenced:

• Building eval systems that improve your AI product: https://www.lennysnewsletter.com/p/building-eval-systems-that-improve

• Mercor: https://mercor.com/

• Brendan Foody on LinkedIn: https://www.linkedin.com/in/brendan-foody-2995ab10b

• Nurture Boss: https://nurtureboss.io/

• Braintrust: https://www.braintrust.dev/

• Andrew Ng on X: https://x.com/andrewyng

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