| I would argue the exact opposite. Language specifics you can look up and confirm easily. But recent gpt-4o tried to convince me that Python added a pipe operator in 3.13. Even had sources. To my disappointment, that's just a lie. (https://chatgpt.com/share/67de9c77-d5f4-8012-9f1c-ac15b70aee...) On the other hand, intuition and thought process is something I have good experience with ChatGPT, ie deciding on architecture (tRPC vs gRPC vs REST for my use case). I would say good use: generating small code snippets, architecture decisions Bad use: Anything documentation related, any specific feature, any nitpicks. (Just ask the security guys how good chatgpt is at paying attention to the little things) Anything you can look up in docs / ref. Anything where there is a clear yes / no answer Note: A good use of LLMs imho is trying to get a start point for lookup docs, like "What's that thing in Python like [x for x in...] called and where can I find more info". If you however ask it for exact rules for list comprehension it's gonna tell you lies sometimes Edit2: Unless you mean like really general language specifics. Like how do I make classes in Ruby. In that case yeah that works |
I like this and resonate with it a lot. Sometimes you don’t know what you don’t know or you you just a little about what you might not know. This helps at least give you a name for the thing which you can then verify from source.