The Wisdom Wall

“It's just that the value proposition of publishing an eval is completely orthogonal to the value proposition of building evals in service of building a good product. I think the purpose of publishing benchmarks is marketing, and it's, it's good marketing.”

“in many ways coming to the table of product building with representative examples and criteria that articulate what good versus bad is for a use case is just a more precise and usable form of product management than writing a spec.”

“The problem is that the challenge in deploying vector search has very little to do with vector search itself, and much more to do with the data adjacent to vector search. So, for example, if you are at Figma, the Vector search is not actually the hard problem. It is the permissions, and who has access to what design…”

“if you make assumptions about the capabilities of models, and you engineer around them, you're almost, like, guaranteed to be screwed.”

“People don't really want to create golden data sets. It's, I think it's, it's, it's often a wasted effort to, to the point that you're making. I think the best teams view offline evals as a mechanism of reconciling what they see in production with real users who are using the product with Tests and iteration that they…”

“You need to have like a pretty specific expertise to design the RL environment in a way that's not vulnerable to reward hacking. And I think that either you'll end up with some, um, fixed number of very well engineered RL environments, or you need to somehow employ this expertise to apply it to the end plus first…”

“It is very, very hard to motivate a large organization to prioritize the problem. And so you're always going to be a second or third tier priority.”

“In my limited experience and working with designers myself, I think designers do not want AI to design things for them. But there's a lot of things that aren't in the traditional designer toolkit that AI can solve. And I think the biggest one is generating code. So in my mind, there's this very interesting convergence…”

“if you structure your code so that the same function abstraction that you define to evaluate on equals equals the abstraction that you actually use to run your application, then when you log your application itself, you actually log it in exactly the right format to do evals”

“cursor is taking AI and And making traditional software engineering like insanely good with AI. And we are taking some of the best things about traditional software engineering and bringing them to building AI software.”

“for it to be a business, you need to align with the problem, not the technology. And I think that Automatic optimization is a really great business problem to solve. And I think if you're too fixated on fine tuning as the solution to that problem, then you're very vulnerable to technological shifts.”

“If you build your system in a way that Kind of assumes LLMs will get better at reasoning and get better at sort of agentic tasks in the LLM itself. Then I think you will build a more durable system.”

“offline evals, which I think is what a lot of the debate was about are both the most challenging to build feedback loop and also the most efficient once built. And then I think AB tests are a little bit less challenging to build and a little bit less efficient. And then like pure vibes are, Basically like zero effort…”

“Like if you construct evals in a way that represent the true north star of the problem that you're solving, then they tend not to break as you change the underlying system. Whereas if you hard code them to a narrow subset of like an implementation detail of your problem, they can be a very, very brittle.”

“However, I learned firsthand that if you don't have a very deep, intuitive understanding of your customer, everything becomes harder. Like, you need to throw product managers at the problem. Your own ability to see around corners is much weaker. And, you know, depending on who you are, it might actually be very…”

“And three, Figma is kind of like Apple, a company that is really optimized around a periodic, like annual release, uh, cycle rather than something that's continuous.”

“And I think in the absence of evals, what I saw at Impera, and I see with almost all of our Customers before they start using brain trust is this kind of like stalemate between people on which prompt to use or which model to use or which technique to use that once you sort of embrace engineering around evals, it just…”

“our core belief is that if you embrace evaluation as The sort of core workflow in AI engineering, meaning every time you make a change, you evaluate it, and you use that to drive the next set of changes that you make, then you're able to build much, much better AI software.”

“At the time, and still, like, AI is sort of at least nominally dominated by Python, but product building is dominated by TypeScript.”

“So in my observation, database companies tend to succeed when the storage paradigm Is closely tied to the execution paradigm, and both of those things need to be rewired to work. I think, remember, the databases are not just storage, but they're also compilers, and it's the fact that you need to build a compiler that…”

“the idea is like, it's useful to write the eval before you actually like tweak the prompt so that you can, um, measure the impact of the tweak.”