High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / evaluation

Published · transcript-backed

Shreya Shankar: evaluation

25 Sept 2025 Lenny's Podcast Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course)

“I think the products that are doing this, they have a very sharp sense of how well their application is performing, and people don't talk about it, because this is their moat.”

— Shreya Shankar

Source trail

Everything needed to verify it.

Speaker
Shreya Shankar
Attribution
Verified speaker
Claim type
evaluation
Recorded
25 Sept 2025
Publisher
Lenny's Podcast

Transcript context

…Give us just a quick sense of what comes next and then let's talk about the debate around evals and a couple more things. What comes next after you've built your LLM judge? Well, we find that people just try to use that everywhere they can, so they'll put the LLM judge in unit tests and they will build, "Here are some example traces where we saw that failure, because we labeled it. Now we're going to make those part of unit tests and make sure that, every time we push a change to our code, these tests are going to pass." They also use it for online monitoring. People are making dashboards on this, and I think that's incredible. I think the products that are doing this, they have a very sharp sense of how well their application is performing, and people don't talk about it, because this is their moat. People are not going to go and share all of these things, because it makes sense. If you are an email-writing assistant, and you're doing this and you're doing it well, you don't want somebody else to go and build an email-writing assistant and then get you out of business. I really want to stress the point that it's try to use these artifacts that you're building wherever possible online, repeatedly use them to drive improvements to your product. Oftentimes, Hamel and I will tell people how to do this up to this very point, and it clicks for people and then they never come back again. Either they have, I don't know, quit their jobs, they're not doing AI development anymore, or they know what to do from here on out. I think it's the latter, but I think it's very powerful. Just watching you do this really opened my eyes to what this is and how systematic the process is. I always imagine you just sit on a computer, "Okay, what are the things I need to make sure work correctly?" What you're showing us here is it's a very simple step-by-step based on real things that are happening in your product, how to catch them, identify them, prioritize them, and then catch them if they happen again and fix them.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence