Eval engineering in AI

By @edhonour · Instagram

Source: https://www.instagram.com/reel/DdA_Pv6pvSH/?stkn=ZjZlajkxMHY1NHRx

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Summary

Eval engineering is emerging as one of the most in-demand AI roles because someone has to prove whether an AI application actually works, especially when swapping models. It sits closer to data science than prompting, since it forces statistical rigor onto a non-deterministic system.

Core ideas

One context card plus 5 ideas

Eval engineering over hype roles: Prompt engineering, context engineering, and harness engineering get the attention, but none of them matter if nobody can measure whether the AI application works.
Measuring improvement or decay: The core job is tracking whether an AI application is getting better or worse over time, not just whether it works once.
Model swap testing: A common use case is checking how switching to a cheaper or different model changes output quality before committing to the change.
Data science lens: This role pulls from data science because it applies statistical, deterministic-style evaluation methods to a system that is fundamentally non-deterministic.
Early but rising: The speaker frames this as a role most people have not heard of yet, but expects it to become widely discussed.

Quotes

“So one of the most in-demand roles in AI right now is called eval engineering.”
@edhonour

“None of that really matters if you can't determine whether or not your AI application works or not.”
@edhonour

“This is really more of a data science role because you're trying to get statistical deterministic type evaluations on something that is non-deterministic.”
@edhonour

“If this is the first time you've heard about eval engineering, you are definitely going to be hearing a lot about it in the future.”
@edhonour

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