I must be the dumbest "prompt engineer" ever, each time I ask an AI to fix or even worse, create something from scratch it rarely returns the right answer and when asked for modification it will struggle even more. All the incredible performance and success stories always come from these Twitter posts, I do find value in asking simple but tedious task like a small refactor or generate commands, but this "AI takes the…
I've found AI to be useful on precisely-scoped tasks I might assign to a junior programmer to take a day to do, like "convert this exact bash script to a Powershell script". But in my own work, those tasks are pretty rare, like 3 times a month? Often I start working on something, and the scope and definition of success changes while I'm in the midst of it. Or it turns out to be harder than expected and it makes sense…
I once accidentally asked a local DeepSeek distilled model to do the wrong thing by accidentally copy pasting the wrong variable name.
It told me how to do it, and then asked me if I was sure.
My local DeepSeek R1 model (deepseek-r1:7b) saw me trying to do something stuupid (I was working with the wrong variable). It told me how to do what I asked and then asked:
> _Is this modification part of a larger change you're making to the code? I'd like to make sure we're not modifying something important that might have side effects._
Looking at its though process:
> _The user wants to modify this string by replacing "-input" with "-kb". But looking at the ARN structure, I recall that AWS S3 ARNs are usually in the form arns3:::bucket_name RegionalPart path. The part after the bucket name is typically s3:// followed by the object key._ > _Wait, maybe the user has a specific reason to make this change. Perhaps they're formatting or structuring something for a different purpose. They might not realize that directly modifying ARNs can cause issues elsewhere if not done correctly._