I also have felt like these kinds of efforts at instructions and agent files have been worthwhile, but I am increasingly of the opinion that such feelings represent self-delusion from seeing and expecting certain things aided by a tool that always agrees with my, or its, take on utility. The agent.md file looks like it’d work, it looks how you’d expect, but then it fails over and over. And the process of tweaking is pleasant chatting with supportive supposed insights and solutions, which means hours of fiddling with meta-documentation without clear rewards because of partial adherence.

The papers conclusions align with my personal experiments at managing a small knowledge base with LLM rules. The application of rules was inconsistent, the execution of them fickle, and fundamental changes in processing would happen from week-to-week as the model usage was tweaked. But, rule tweaking always felt good. The LLM said it would work better, and the LLM said it had read and understood the instructions and the LLM said it would apply them… I felt like I understoood how best to deliver data to the LLMs, only to see recurrent failures.

LLMs lie. They have no idea, no data, and no insights into specific areas, but they’ll make pleasant reality-adjacent fiction. Since chatting is seductive, and our time sense is impacted by talking, I think the normal time versus productivity sense is further pulled out of ehack. Devs are notoriously bad at estimating where they’re using time, long feedback loops filled with phone time and slow ass conversation don’t help.