At least with practical implementations you can still verify the output through tests or trial and error but it becomes even more fragile when asking about facts or knowledge.
But I have had incredibly useful conversations on broad strokes stuff. A question like "what are some of the various options for scoring the similarity between news stories for the purpose of showing similar content to a user" or something can be really really useful, much more so than trying to find that perfect blog post about it, because you can ask follow up questions and sort of evaluate the various key words and concepts you'll want to eventually learn.
Stuff like that.
Hallucinations are still a problem. The AI doesn't actually know "what options are useful for scoring ...", it's just regurgitating what someone else told it and when you ask it follow up questions it's unable to reason.
AI's main feature is the first thing we disqualify people for in interviews. It's knowledge is pure buzzwords and hype.
Example: I just asked chatGPT to tell me why framework X is better than framework Y. I am familiar with both frameworks. After the list chatGPT gives a disclaimer that starts with "However, it's essential to note that Y also has its own strengths". It then proceeds to list a few features that both frameworks share, a few features that are subjectively better and a few features that are actually bugs/code smell.
That's not the AI's comparison of the two frameworks, that is just someones blog post that has been regurgitated to me in a way that gets around copyright.