Ask HN: How do you know if a tweak to your AI skill made it better?
Curious how people here evaluate changes when they tweak an AI skill / prompt / workflow.
A lot of the time, a tweak might feel better in one or two cases, but it’s hard to tell if it actually improved the skill overall or just changed its behavior in a way that looks better for a bit.
Do you mostly go by intuition, or do you have some lightweight way to check if a tweak really helped? In general, you don’t know. Sure thing if you have a specific code base in which you already had a bunch of tests (non ai generated ) and the code you are regenerating is always touching the logic behind those tests, sure you can assess to some extent your skills/prompt changes.
But in general you just don’t know. You havr a bunch of skills md files that who knows how they work if changed a little bit here a little bit there.
People who claim they know are selling snake oil a bit heavier weight, but seems worthwhile if working in an org where many people consume the skill: - find N tasks from your repo that serve as good representation of what you want the agent to do with the task
- run agent with old skill/new skill against those tasks
- measure test pass rate / other quality metrics that you care about with skill
- token usage, speed, alignment, ...
- tests aren't a great measure alone - I've found them to be almost bimodal (most models either pass/fail) and not a good differentiator
- use this to make decisions about what to do with the skill - keep skill A, promote skill B, or keep tweaking I've also had success with an "autoresearch" variant of this, where I have my agent run these tests in a loop and optimize for the scores I'm grading o The way we handle it is keeping a small set of fixed test cases that we never change. Like same inputs, same expected outputs. so when we tweak a prompt we run it against those first. if it passes the fixed cases and feels better on the new ones, we keep it. How you get deterministic output though? t=0? Pydantic AI outputs?