For me it's the same. It's usually just some hallucinated garbage. All of these LLM's don't have the full picture of my project.

When I can give them isolated tasks like convert X to Y, create a foo that does bar it's excellent, but for unit testing? Not even going to try anymore. I write 5 unit tests manually that work in the time I write 5 prompts that give me useless stuff that I need to add manually.

Why can't we have a LLM cache for a project just like I have a build cache? Analyze one particular commit on the main branch very expensively, then only calculate the differences from that point. Pretty much like git works, just for your model.

Ask Cursor to write usage and mocking documentation for the most important injected dependencies, then include that documentation in your context. I’ve got a large tree of such documentation in my docs folder specifically for guiding AI. Cursor’s Notebook feature can bundle together contexts.

I use Cursor to work on a Rust Qt app that uses the main branch of cxx-qt so it’s definitely not in the training data, but Claude figures out how to write correct Rust code based on the included documentation no problem, including the dependency injection I do through QmlEngine.