Mathematical reasoning only exists associativity in any pure language model so inherently any tasks that requires any sort of mathematical complexity isn't one

Mathematical reasoning only exists associativity in any pure language model so inherently any tasks that requires any sort of mathematical complexity isn't one you should be approaching without at least defining the logic, algo, interfaces, inputs, expected outputs, tests etc. yourself first IMO. With that being said, IMO, you can optimize your dev work to get the most use from GPT4 by providing better context, examples etc. these don't really fit into the co-pilot workflow but can fit into instruction-tuned model workflows. (GPT4 / Mixtral / Mistral / CodeLLAMA etc.) E.g. use the first 50k tokens in the context window to pre-prompt with relevant context from your code repo/sdk documentation/examples whatever prior to actually prompting with your task. And demonstrate a test in your prompt that displays what you want. Taking a "few-shot" approach with specified validation (in your test) results in considerable improvements in accuracy for programming (and other) tasks. (YMMV)