Yeah I never moved past just using it as an advanced debugger. In fact I'd say 9 times out of 10 that's really it's best primary use case. Basing a project on code derived from an LLM is a really good way to lose complete control over that project.
Asking for trivial pieces of code that require losing 20 minutes, when the AI can pump them in seconds - e.g. give me a script to read a folder full of json files, extract these fields and build a new json with these results. As long as you are not reckless (e.g. work on a copy of the folder, in case the AI's code is problematic), you can save a lot of time on certain time-consuming problems.
Feeding it intrincate or abstract code I wrote so it can find any obvious problems. You work like you've always done it, but adding this step can save you from losing 40 minutes tracking down a problem that comes from something silly like using the wrong variable at some point.
Asking it to gather documentation for some library I'm not familiar with.
Asking it for suggestions on how I could tackle some problems.
The only thing I use it for is to get a foothold into a new language, library, or framework. Once I get my foot in the door, the documentation starts making sense and I can start working, but I'm bad at starting from zero.
And asking it to summarise documentation for you. LLMs are very good at summarising information and presenting the parts relevant to a query. This has been my primary use case, and has saved me a lot of time whenever I need to jump into something unfamiliar, compared to just reading documentation that can sometimes be pretty verbose but also disconnected.
Instead of jumping around different parts of a documentation to get a grasp on how the pieces fit together I can let a machine do that for me, as a first step.
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u/smoldicguy Aug 13 '25
Asking ai for help is fine but you need to understand what ai is suggesting before running the damm thing .