[inthebin]

I think it's your mindset and how you approach it. E.g. some people are genuinely bad at googling their way to a solution. While some people know exactly how to manipulate the google search due to years of experience debugging problems. Some people will be really good at squeezing out the right output from ChatGPT/Copilot and utilize it to maximum potential, while others simply won't make the connection.

Its output depends on your input.

E.g. say you have an API swagger documentation and you want to generate a Typescript type definition using that data, you just copy paste the docs into a comment above the type, and copilot auto fills your Typescript type definition even adding ? for properties which are not required.

If you define clearly the goal of a function in a JSDoc comment, you can implement very complex functions. E.g. you define it in steps, and in the function line out each step. This also helps your own thinking. With GPT 4o you can even draw diagrams in e.g. excalidraw or take screenshots of the issues in your UI to complement your question relating to that code.

[epiccoleman]

> some people know exactly how to manipulate the google search due to years of experience debugging problems

this really rings true for me. especially as a junior, I always thought one of my best skills was that I was good at Googling. I was able to come up with good queries and find some page that would help. Sometimes, a search would be simple enough that you could just grab a line of code right off the page, but most of the time (especially with StackOverflow) the best approach was to read through a few different sources and pick and choose what was useful to the situation, synthesizing a solution. Depending on how complicated the problem was, that process might have occurred in a single step or in multiple iterations.

So I've found LLMs to be a handy tool for making that process quicker. It's rare that the LLM will write the exact code I need - though of course some queries are simple enough to make that possible. But I can sort of prime the conversation in the right direction and get into a state where I can get useful answers to questions. I don't have any particular knowledge on AI that helps me do that, just a kind of general intuition for how to phrase questions and follow-ups to get output that's helpful.

I still have to be the filter - the LLM is happy to bullshit you - but that's not really a sea change from trying to Google around to figure out a problem. LLMs seem like an overall upgrade to that specific process of engineering to me, and that's a pretty useful tool!

[wiether]

Keep in mind that Google's results are also much worse than they used to be.

I'm using both Kagi & LLM; depending on my need, I'll prefer one or the other.

Maybe I can access the same result with a LLM, but all the conversation/guidance required is time-consuming than just refining a search query and browsing through the first three results.

After all the answer is rarely exactly available somewhere. Reading people's questions/replies will provide a clues to find the actual answer I was looking for.

I have yet been able to achieve this result through a LLM.

[DiscourseFan]

> E.g. you define it in steps, and in the function line out each step. This also helps your own thinking

Yeah but there are other ways to think through problems, like asking other people what they think, which you can evaluate based on who they are and what they know. GPT is like getting advice from a cross-section of everyone in the world (and you don’t even know which one), which may be helpful depending on the question and the “people” answering it, but it may also be extroadinarily unhelpful, especially for very specialized tasks (and specialized tasks are where the profit is).

Like most people, I have knowledge of things very specific I know that less than a 100 people in the world know better than me, but thousands or even millions more have some poorly concieved general idea about it.

If you asked GPT to give you an answer to a question it would bias those millions, the statistically greater quantative solution, to the qualitative one. But, maybe, GPT only has a few really good indexes in its training data that it uses for its response, and then its extremely helpful because its like accidentally landing on a stackoverflow response by some crazy genius who reads all day, lives out of a van in the woods, and uses public library computers to answer queries in his spare time. But that’s sheer luck, and no more so than a regular search will get you.