| ▲ | chermi 5 hours ago | ||||||||||||||||||||||
This lands. Llms are bad precisely at following what not to do. They work best off of positive constraints. I have a design principles + tech preferences doc I force llms "lint" their approach against. It's not perfect but it helps. I call it a bias field, pushes them toward hopefully the happy and harmonious (with the rest of the system) paths. Obviously this is only partial and imperfect enforcement, but if it's applied to everything consistently it naturally encodes some self-consistency and harmony. | |||||||||||||||||||||||
| ▲ | datsci_est_2015 4 hours ago | parent | next [-] | ||||||||||||||||||||||
> I have a design principles + tech preferences doc I force llms "lint" their approach against. It's not perfect but it helps. I’ve done the same but it’s a moving target as models advance and I find half of my points are ignored until I’m prompting “No wtf why are you still trying to symlink the global Python executable just use the virtual environment that’s already activated”. Anyway, companies are pouring billions into improving AI tooling user experience so most of what I do manually I just anticipate to be a waste of time. There’s no way my hobby fiddling will outpace whatever gets released in the next couple months. In the meantime, real linting does work pretty well, if you can write a detector for whatever antipattern you find LLMs fall into (like multiline comments). | |||||||||||||||||||||||
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| ▲ | BatFastard 4 hours ago | parent | prev [-] | ||||||||||||||||||||||
I would be interested in see that. I have reems of rules I use with Claude. | |||||||||||||||||||||||