That works for well trod paths, e.g “fix ci” works exceedingly well. “why app slow” obviously doesn’t work because the task is underspecified. But in order to properly specify you either need an experienced engineer who knows how to narrow the problem domain, or you have to provide some template instructions/output formats (e.g, skills) which will invariably never fit the problem perfectly
I wouldn't agree. Sota models can do self-directed sampling, profiling, benchmarking, read call trees, etc. to give you a report of the app's bottlenecks and then recommend solutions that can be vetted.
I do this constantly.
As the upstream comment points you, you don't need to specify. Sota models are that good. And by being overprescriptive you can accidentally shut off branches that they would've taken, downgrading the quality of their work.
I use skills. The skills are not typically "how to perform a task in detail" they are more about what relevant tools and knowledge are required to work in a domain. That is I give the LLM the information it needs about the system but not a sequence of how to accomplish a task. I treat it more like a human and less like a computer.
It really doesn’t need to be that much more specified, give it context to the tools and level of analysis you expect then “why app slow” is a reasonable prompt
> . “why app slow” obviously doesn’t work because the task is underspecified.
Not always. In my case LLM goes to grafana mcp, pulls metrics/traces/cpu profiles. Figures out what is slow and proposes a solution.