[mikaelaast]

Sure. Verifiability is far-fetched. But say I want to produce a statistically significant evaluation result from this – essentially testing a piece of prose. How do I go about this, short of relying on a vague LLM-as-a-judge metric? What are the parameters?

[visarga]

You 100% need to test work done by AI, if it's code it needs to pass extensive tests, if it's just a question answered, it needs to be the common conclusion of multiple independent agents. You can trust a single AI as much as a HN or reddit comment, but you can trust a committee of 4 as a real expert.

More generally I think testing AI by using its web search, code execution and ensembling is the missing ingredient to increased usage. We need to define the opposite of AI work - what validates it. This is hard, but once done you can trust the system and it becomes cheaper to change.

[JamesSwift]

How would you evaluate it if the agent were not a fuzzy logic machine?

The issue isnt the LLM, its that verification is actually the hard part. In any case, its typically called “evals” and you can probably craft a test harness to evaluate these if you think about it hard enough

[coldtea]

Would a structured skills file format help you evaluate the results more?

[mikaelaast]

Yes. It would make it much easier to evaluate results if the input contents were parameterized and normalized to some agreed-upon structure.

Not to mention the advantages it would present for iteration and improvement.

[coldtea]

"if the input contents were parameterized and normalized to some agreed-upon structure"

Just the format would be. There's no rigid structure that gets any preferrential treatment by the LLM, even if it did accept. In the end it's just instructions that are no different in any way from the prompt text.

And nothing stops you from making a "parameterized and normalized to some agreed-upon structure" and passing it directly to the LLM as skills content, or parsing it and dumping it as skills regular text content.