A small thing, but I found the author's project-organization practices useful – creating individual .prompt files for system prompt, background information, and auxiliary instructions [1], and then running it through `llm`. It reveals how good LLM use, like any other engineering tool, requires good engineering thinking – methodical, and oriented around thoughtful specifications that balance design constraints – for b…
How do we benchmark these different methodologies? It all seems like vibes-based incantations. "You are an expert at finding vulnerabilities." "Please report only real vulnerabilities, not any false positives." Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
1. Having workflows to be able to provide meaningful context quickly. Very helpful.
2. Arbitrary incantations.
I think No. 2 may provide some random amounts of value with one model and not the other, but as a practitioner you shouldn't need to worry about it long-term. Patterns models pay attention to will change over time, especially as they become more capable. No. 1 is where the value is at.
As my example as a systems grad student, I find it a lot more useful to maintain a project wiki with LLMs in the picture. It makes coordinating with human collaborators easier too, and I just copy paste the entire wiki before beginning a conversation. Any time I have a back-and-forth with an LLM about some design discussions that I want archived, I ask them to emit markdown which I then copy paste into the wiki. It's not perfectly organized but it keeps the key bits there and makes generating papers etc. that much easier.