Somewhat tailored response per your username: Anything the ministry of truth decides should not be part of the training set.
But more generally, even in things like neurodivergence, lots of questions you could ask that would be in the bounds of "guesstimation" by professionals. The problem is these LLM will spout pure utter highly-believably-confident snake oil with the tiniest little legal microscopic fineprint on the side of the html webpage that you can never scroll to saying "AI responses can be wrong".
For me, the mere fact that you have a nicely worded source of information that, depending if you're lucky with your question, is pure scientific fact, or utter maddening bullshit, makes me terrified to use it.
Exactly same thinking. I can usually type it out faster than AI (when taking into account latency, context building, prompt writing, prompt fixing, AI hallucination review) if I know what exactly needs to be done.
When you're out of your depth, it shines in providing beautifully confident and a botanical garden of a code piece that wil most likely be broken and break in new and exciting ways. Great for incompetent sycophantically-challenged managers that forgot how to code, or never actually had any experience.
Large-scale code transformations, taking into account the bigger picture of the repository are most likely hallucination free (not generative, just transformative, as per the actual LLM model) and a very exciting use-case.
Also it's currently a very nice pragmatic tool for checking for any mistakes, because it can connect the larger context of the repository quickly to the diff. Terrible if done by a manager without understanding of copilot and workflow, but a great tool if done through claude and cli as a pre-commit step with just quick checklists. Makes plenty of mistakes, but allows to catch your own big mistakes nicely quickly.