[xmcqdpt2]

This is useful when selecting a model for an initial application. The main issue I'm concerned about though is ongoing testing. At work we have devs slinging prompt changes left and right into prod, after "it works on my machine" local testing. It's like saying the words "AI" is sufficient to get rid of all engineering knowledge.

Where is TDD for prompt engineering? Does it exist already?

[lorey]

This is a very good point. When I came in, the founder did a lot of evaluation based on a few prompts and with manual evaluation, exactly as described. Showing the results helped me underline the fact that "works for me" (tm) does not match the actual data in many cases.

[cap11235]

Evals have always existed, and not using them when building systems is relying on superstition.

[lorey]

This is true with one caveat.

In most cases, e.g. with regular ML, evals are easy and not doing them results in inferior performance. With LLMs, especially frontier LLMs, this has flipped. Not doing them will likely give you alight performance and at the same time proper benchmarks are tricky to implement.