I've been diving into AI-assisted coding workflows and I'm noticing some consistent challenges. For instance, the AI often lacks a broader awareness of the entire codebase, struggles with setups that involve multiple services or repositories, and has trouble reasoning when the context changes. Additionally, switching tools can feel like I'm starting from scratch every time. I'm really interested to know if others are experiencing similar issues. What shortcomings do you see in these tools?
5 Answers
The AI really struggles with leaving relevant comments in the code. Having well-placed human comments and even commented-out sections for reference is crucial, yet it fails to do that.
Honestly, it’s fantastic for analysis and discovery but falls short when it comes to actually building systems. If it doesn’t know how to do something initially, it rarely improves with repeated prompts.
One major issue I've found is that when the AI encounters a problem, it seldom steps back to reassess the situation. Instead, it tends to push forward with increasingly complex solutions that often miss the mark. If it gets something wrong, it seems less likely to provide a proper fix on the next try, yet it remains oddly confident in its suggestions.
For me, anything that requires more context than a short description tends to fall apart. However, I find it incredibly useful for generating boilerplate code or troubleshooting specific errors. It's also quite handy for drafting requirements documents.
I totally relate! It almost feels like it tries to fix the fixes, which just creates more confusion. Sometimes I have to remind it to take a step back and reevaluate the entire problem.
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