I've been hearing a lot about using LLMs, like GPT, along with various MCP servers for troubleshooting Kubernetes. I've tried a few, and while they can catch simple mistakes like misspelled image names or wrong ports, they haven't really helped much with more complex issues. I'd love to know what others think about their effectiveness and if you've had better experiences.
5 Answers
I’ve been able to get decent suggestions for simpler issues or when I'm just stuck for ideas. But for anything nuanced, I still rely on my understanding of Kubernetes and prefer the good old docs. They’re a resource, but only if you know how to ask the right questions.
I've had mixed results. I enjoy using LLMs for light code reviews and quick error checks, but I wouldn't rely on them for deep dives or root cause analyses. For instance, they can help set up a simple environment or pick up low-hanging errors, but when it comes to interacting with complex IaC, they struggle a lot. They definitely shouldn’t replace your usual debugging methods, but they can be a handy tool in your kit if used right.
For me, using LLMs has been a bit like having an eager but inexperienced assistant. They can occasionally lead you in the right direction with log errors, but ultimately, you're the one doing the heavy lifting. If you give them clear prompts, they can help, but they can also mislead if not managed correctly.
Honestly, I've found LLMs to be pretty underwhelming for serious troubleshooting. They seem great for beginners who need help navigating documentation, but if you're doing anything complex, you'll probably want to keep your docs handy anyway. Using one feels a bit like mentoring a clueless intern – unless you're super specific, the results can be questionable.
From my experience, LLMs are best for quick suggestions or ideas about what's wrong but don’t expect them to get it right without your expertise. If you know what you’re doing, they can save time, but if not, they can waste hours with irrelevant guesses. For basic coding or PR reviews, I find them pretty effective though!
Completely agree! Sometimes they excel at pulling together logs and forming hypotheses quickly, but if you don’t guide them with clear inputs, the output can be all over the place.