Ask HN: How are you getting reliable code-gen performance out of LLMs?

[_false]

I'm particularly interested in people using LLM APIs, where code is consumed programmatically.

I've been using LLMs a lot lately to generate code, and code quality is a mixed bag. Sometimes it will run straight out of the box or with a few manual tweaks, and others it just straight up won't compile. Keen to hear what workarounds others have used to solve this (e.g. re-prompting, constraining generations, etc).

[ilaksh]

Your post would make more sense to me if you were specific about the models. It's like if you were asking about how to get reliable transportation from a car and didn't specify which model of cars you were considering.

o1-preview seems to be a step up from Claude 3.5 Sonnet.

There are many open source coding LLMs that for complex tasks will be a joke compared to the SOTA closed ones.

I think that there are two strategies that can work: 1) constrain the domain to a particular framework and provide good documentation and examples in the prompts for it, and 2) create an error-correcting feedback loop where compilation/static analysis and runtime errors or failed tests are fed back to the model automatically.