zlacker

>>tehnub+(OP)
I'm biased by my preferred style of programming languages but I think that pure statically typed functional languages are incredibly well suited for LLMs. The purity gives you referential transparency and static analysis powers that the LLM can leverage to stay correctly on task.

The high level declarative nature and type driven development style of languages like Haskell also make it really easy for an experienced developer to review and validate the output of the LLM.

Early on in the GPT era I had really bad experiences generating Haskell code with LLMs but I think that the combination of improved models, increased context size, and agentic tooling has allowed LLMs to really take advantage of functional programming.

>>solomo+sk
I'm inclined to agree with you in principle, but there's much, much less Haskell examples in their training corpus than for JavaScript or Python.

>>eru+bl
From what I've heard—and in my own very limited experiments—LLMs are much better at less popular languages than I would have expected. I've had good results with OCaml, and I've talked to people who've had good results with Haskell and even Unison.

I've also seen multiple startups that have had some pretty impressive performance with Lean and Rocq.

My current theory is that as long as the LLM has sufficiently good baseline performance in a language, the kind of scaffolding and tooling you can build around the pure code generation will have an outsize effect, and languages with expressive type systems have a pretty direct advantage there: types can constrain and give immediate feedback to your system, letting you iterate the LLM generation faster and at a higher level than you could otherwise.

I recently saw a paper[1] about using types to directly constrain LLM output. The paper used TypeScript, but it seems like the same approach would work well with other typed languages as well. Approaches like that make generating typed code with LLMs even more promising.

Abstract:

> Language models (LMs) can generate code but cannot guarantee its correctness often producing outputs that violate type safety, program invariants, or other semantic properties. Constrained decoding offers a solution by restricting generation to only produce programs that satisfy user-defined properties. However, existing methods are either limited to syntactic constraints or rely on brittle, ad hoc encodings of semantic properties over token sequences rather than program structure.

> We present ChopChop, the first programmable framework for constraining the output of LMs with respect to semantic properties. ChopChop introduces a principled way to construct constrained decoders based on analyzing the space of programs a prefix represents. It formulates this analysis as a realizability problem which is solved via coinduction, connecting token-level generation with structural reasoning over programs. We demonstrate ChopChop's generality by using it to enforce (1) equivalence to a reference program and (2) type safety. Across a range of models and tasks, ChopChop improves success rates while maintaining practical decoding latency.

[1]: https://arxiv.org/abs/2509.00360

>>tikhon+DJ
As a huge proponent of constrained decoding for LLM reliability, I don't quite think it's the right approach for code. This is because in many programming languages, it is legal to use a function before its declaration. Since this is common in existing code, LLMs will also try to write code that way.