31–40 of 66 posts
evals are glorified integration tests, would you invest in an integration test startup? absolutely not. I don't get why we are making all of this fuzz around evals
It's simple to say but hard to master doing well, and the important thing is that no matter what tool you have the evals don't write themselves.
Maybe it's not that valuable? No snark, but how much confidence do these evals provide?
Earlier quoted context omitted.
"Which frontier model is best?" What kind of stupid business is this. Though nothing can beat SEO in that spirit.
It's an important question! If you are paying a lot of money to use AI models, you care that you are using the best for your task. And it turns out that figuring out which AI models is best for your task is not trivial and requires some expertise.
What's an eval?
What makes it particular for the case of AI is:
- there are many situations where you can’t test using pattern matching
- you don’t only like to test correct answers but voice and tone too (imagine a bank support LLM-based chatbot that answers using slang)
- evals can be used to compare the performance of different implementations; given the costs of LLMs, it’s very important
- running evals is more expensive than running standard tests, because you rely on the LLM calls under test, and many times they use LLMs as a judge. It means that running them in every commit of your CI/CD is very expensive
- Knowing all the possible inputs for the LLM is impossible, so evals can also be run on runtime samples to detect anomalies
What's an eval?
(Author) It's short for "evaluation", a test for an AI model. Specifically, an AI evaluation comprises (1) a dataset of prompts (as questions / tasks / queries), (2) some way to score model performance on each prompt, like a set of correct answers or a grading rubric that you can use with an LLM autograder, and (3) a metric, such as accuracy¹. (If you're already familiar with the term "benchmark", it's the same thing…
evals are glorified integration tests, would you invest in an integration test startup? absolutely not. I don't get why we are making all of this fuzz around evals
Earlier quoted context omitted.
"Which frontier model is best?" What kind of stupid business is this. Though nothing can beat SEO in that spirit.
It's an important question! If you are paying a lot of money to use AI models, you care that you are using the best for your task. And it turns out that figuring out which AI models is best for your task is not trivial and requires some expertise.
Also, model providers are not interested to have their models compared head-to-head under identical conditions. And “Model A is better than Model B” is almost meaningless by itself. Better for what task? With what prompt? What inputs? What budget? What failure tolerance?
It would be nice to have a place where users could run their own benchmarks, define evaluation criteria for their actual use cases, and make those runs verifiable by others.
I built a simple (free) eval tool for my own uses (Github Gists + Model Outputs) after not being able to find a suitable one in the market. The market's being split into 1. Longitudinal LLM observability tooling Most eval startups have gone down the route of something more like being an observability platform for LLM inference. They want to be in your stack and running the inference to collect data on performance of…