Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course)
Episode
106 min
Read time
2 min
Topics
Investing, Fundraising & VC, Leadership
AI-Generated Summary
Key Takeaways
- ✓Error Analysis Process: Start by manually reviewing 100 production traces, writing freeform notes on the first upstream error encountered in each trace. This open coding reveals actual failure modes versus hypothetical ones, taking approximately one week initially then 30 minutes weekly for ongoing monitoring and improvement.
- ✓Benevolent Dictator Approach: Assign one domain expert with product taste to conduct error analysis rather than forming committees. This person should be the product manager for most applications, keeping the process tractable and avoiding expensive consensus-building that prevents teams from executing systematic evaluation workflows.
- ✓Axial Coding with AI: After collecting open codes, use LLMs to categorize notes into actionable failure modes through axial coding. Create pivot tables to count occurrences, revealing the most prevalent issues. This transforms chaos into prioritized problems, though AI cannot replace the initial human error analysis step.
- ✓Binary LLM Judges: Build automated evaluators that output pass/fail for specific failure modes, not Likert scales. Validate judges against human labels using confusion matrices before deployment. These judges monitor production traces continuously, catching issues like inappropriate handoffs or hallucinated features that code-based tests cannot detect.
- ✓Strategic Eval Investment: Write 4-7 LLM judge evaluators for persistent, ambiguous failure modes that resist prompt fixes. Skip evals for obvious engineering errors. The highest ROI activity involves looking at actual user interaction data, which most teams neglect by jumping straight to hypothetical test cases without grounding in real problems.
What It Covers
Hamel Husain and Shreya Shankar, creators of the top-rated Maven evals course, demonstrate systematic methods for building AI product evaluations through error analysis, open coding, and LLM judges to measure and improve application quality beyond vibes-based testing.
Key Questions Answered
- •Error Analysis Process: Start by manually reviewing 100 production traces, writing freeform notes on the first upstream error encountered in each trace. This open coding reveals actual failure modes versus hypothetical ones, taking approximately one week initially then 30 minutes weekly for ongoing monitoring and improvement.
- •Benevolent Dictator Approach: Assign one domain expert with product taste to conduct error analysis rather than forming committees. This person should be the product manager for most applications, keeping the process tractable and avoiding expensive consensus-building that prevents teams from executing systematic evaluation workflows.
- •Axial Coding with AI: After collecting open codes, use LLMs to categorize notes into actionable failure modes through axial coding. Create pivot tables to count occurrences, revealing the most prevalent issues. This transforms chaos into prioritized problems, though AI cannot replace the initial human error analysis step.
- •Binary LLM Judges: Build automated evaluators that output pass/fail for specific failure modes, not Likert scales. Validate judges against human labels using confusion matrices before deployment. These judges monitor production traces continuously, catching issues like inappropriate handoffs or hallucinated features that code-based tests cannot detect.
- •Strategic Eval Investment: Write 4-7 LLM judge evaluators for persistent, ambiguous failure modes that resist prompt fixes. Skip evals for obvious engineering errors. The highest ROI activity involves looking at actual user interaction data, which most teams neglect by jumping straight to hypothetical test cases without grounding in real problems.
Notable Moment
The demonstration revealed how a real estate AI assistant told a prospective tenant no apartments with studies were available, then said thanks and goodbye—completely missing the lead nurturing opportunity. This failure only became visible through systematic trace review, not through vibes or generic benchmarks.
Episode Transcript
To build great AI products, you need to be really good at building evals. It's the highest ROI activity you can engage in. This process is a lot of fun. Everyone that does this immediately gets addicted to it when you're building an AI application. You just learn a lot. What's cool about this is you don't need to do this many, many times. For most products, you do this process once and then you build on it. The goal is not to do evals perfectly. It's to actionably improve your product. I did not realize how much controversy and drama there is around evals. There's a lot of people with very strong opinions. People have been burned by evals in the past. People have done evals badly, then they didn't trust it anymore. And then they're like, oh, I'm anti evals. What are a couple of the most common misconceptions people have with evals? The top one is we live in the age of AI. Can't the AI just eval it? But it doesn't work. A term that you used in your post that I love is this idea of a benevolent dictator. When you're doing this open coding, a lot of teams get bogged down in having a committee do this. For a lot of situations, that's wholly unnecessary. You don't wanna make this process so expensive that you can't do it. You can appoint one person whose taste that you trust. It should be the person with domain expertise. Oftentimes, it is the product manager. Today, my guests are Hamil Hussain and Shreya Shankar, one of the most trendy guests are Hamil Hussain and Shreya Shankar. One of the most trending topics on this podcast over the past year has been the rise of evals. Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders. And since then, this has been a recurring theme across many of the top AI builders I've had on. Two years ago, I had never heard the term evals. Now it's coming up constantly. When was the last time that a new skill emerged that product builders had to get good at to be successful? Hammel and Shreya have played a major role in shifting evals from being an obscure mysterious subject to one of the most necessary skills for AI product builders. They teach the definitive online course on evals, which happens to be the number one course on Maven. They've now taught over 2,000 PMs and engineers across 500 companies, including large swaths of the OpenAI and anthropic teams along with every other major AI lab. In this conversation, we do a lot of show versus tell. We walk through the process of developing an effective eval, explain what the heck evals are and what they look like, address many of the major misconceptions with evals, give you the first few steps you can take to start building …
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