alt Hacker NewsI started doing it months ago and, to be honest, what the agent chooses to do isn’t unpredictable.
The problem is that different people prompt so differently.
For example, I may ask like “test different variations of this annotation on k8s pods of this service on this X cluster because it proves Y theory.”
But you know what my coworker asks? “Test Y theory.” If you were to ask two different junior engineers that, one might try random things on production and the other one might run local tests! It’s such an unguided “do anything you want as long you figure it out” request and the agent reads it like a junior who has not been told any boundaries but has been strongly told “figure it out.”
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> I started doing it months ago and, to be honest, what the agent chooses to do isn’t unpredictable.
You just wrote three paragraphs of text describing why it's unpredictable.
Moreover, for the same prompt on the same machine in a different session it will use a different set of tools.
> But you know what my coworker asks? “Test Y theory.”
It still surprises me when I see people not prompting more specifically and clearly. It not only avoids problems, it's faster, costs less -and just works better.
I recently shared with a friend a multi-hour LLM chat session I'd done because it veered into a domain he's interested in. In the session I'd brainstormed and probed the feasibility of a novel concept for a new research direction. It traversed a half dozen domains diving into minute detail then zooming back out to survey an adjacent space, interspersed with intense skeptical probing of key assumptions, all while spewing tons of detailed citations, specific paragraph pulls, summarized data tables etc.
My friend is very experienced using LLMs for research so I was surprised when he called me shocked by the sheer velocity, precise targeting and signal/noise. I'd assumed everyone did it the same as I do. He attributed the different result solely to the way I crafted my prompts.