In this article, we’ll explore why LLM evaluation is challenging, the different types of evaluations available, key concepts to understand, and practical guidance on setting up an evaluation process.
This is a great summary. I’ve been thinking deeply about LLM evals and recently ran some analysis on a small clinical dataset focused on safety. This will become foundational as we move toward AI-first workflows. Similar to test-driven development, evals and evaluation infrastructure will define development quality, speed, and ultimately product outcomes.
I explored some of this in a clinical context here:
Finally! A post talking about evals and the multipronged approach it takes - giving folks options and next steps to be more responsible with AI. Excellent post!
The bit about probabilistic outputs is really what makes LLM evals different from regular testing. It took me a while to wrap my head around this when building with AI.
Thanks ByteByteGo team, another great technical LLM Eval guidance with super clear structure and nice flow!
This is a great summary. I’ve been thinking deeply about LLM evals and recently ran some analysis on a small clinical dataset focused on safety. This will become foundational as we move toward AI-first workflows. Similar to test-driven development, evals and evaluation infrastructure will define development quality, speed, and ultimately product outcomes.
I explored some of this in a clinical context here:
https://medium.com/@deeps.subramaniam/what-happens-when-you-ask-an-ai-a-medical-question-865eb7b62b46
Finally! A post talking about evals and the multipronged approach it takes - giving folks options and next steps to be more responsible with AI. Excellent post!
The bit about probabilistic outputs is really what makes LLM evals different from regular testing. It took me a while to wrap my head around this when building with AI.
Great overview 👏