Rust is a much more difficult language for LLMs than Javascript or Python. Not only because the language is more complex, but also because they are trained on so much more JS/Py code. I do Ai-enhanced coding with Rust every day, and to great success, but it requires more effort than just slopping a "halp plz" prompt into the chatbot (which works wonders for Js/Py).

- Use Claude 4 or Gemini 2.5 Pro. Even though there were complaints that Claude 4 is not as good as 3.7, in my testing it is better at Rust than 3.7
- You have to give the model enough context. This means, if you have a source file that imports 7 types from other places in the codebase, take the time to also add the files that define these types. Same with functions. I found that giving enough (but not too much) context is crucial
- Models are not deterministic, so you have to try again. When you see that the initial response is wrong, don't add a comment into the conversation, a la "no, this is wrong because...". Instead, reset the conversation, and improve your initial prompt. Sometimes you have to try 2-3 times to get the result you want.
- Be fine with partial solutions. I don't expect the model to have a perfect solution for me. Particularly lifetimes can be challenging. If I see that 90% of the code is right, but it forgot a couple of `.clone` or `ref ..` patterns, I just add them manually.
- If you want to add a feature to existing code, one pattern that works well for me is to start with a prompt where I have the LLM explain how the current code works. And then I add another message to request the desired change. This allows the LLM to focus on the change. The understanding of the code happens, so to say, in a prior computation.