alt Hacker NewsWhy do you think LLMs write prompts for LLMs badly? I use LLMs to write and refine prompts all the time. The prompts seem to come out very good. What are you basing this on?
Why do you think LLMs write prompts for LLMs badly? I use LLMs to write and refine prompts all the time. The prompts seem to come out very good. What are you basing this on?
Generally excessively verbose and failing to account for LLM failure modes. Example: when dispatching a subagent with an instruction prompt, I see the agent refer to "phase 5 of the plan" without actually pointing to the plan file it's referencing. The subagent says something to the tune of "phase 5, sure thing boss" and goes and generates garbage because it never reads the plan to find out what "phase 5" entails.
If I ask an agent to maintain agent-facing docs like AGENTS.md then similarly it ends up with low information density and not really saying what I want it to say. Something I thought was unequivocal ends up leaving the agent with far too much scope for interpretation and judgement calls.
Perhaps I'm just Prompting It Wrong (TM) but I do find LLMs lack the theory of mind to realise that other LLM instances don't know the things that the current instance knows, and make poor calls on what to include and what not to include. It's a bit hand-wavey, but I was wondering if the same issue might transfer to language design.