alt Hacker News> Learning how to use LLMs in a coding workflow is trivial. There is no learning curve. You can safely ignore them if they don’t fit your workflows at the moment.
That's a wild statement. I'm now extremely productive with LLMs in my core codebases, but it took a lot of practice to get it right and repeatable. There's a lot of little contextual details you need to learn how to control so the LLM makes the right choices.
Whenever I start working in a new code base, it takes a a non-trivial amount of time to ramp back up to full LLM productivity.
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He’s not wrong.
Getting 80% of the benefit of LLMs is trivial. You can ask it for some functions or to write a suite of unit tests and you’re done.
The last 20%, while possible to attain, is ultimately not worth it for the amount of time you spend in context hells. You can just do it yourself faster.
> That's a wild statement. I'm now extremely productive with LLMs in my core codebases, but it took a lot of practice to get it right and repeatable. There's a lot of little contextual details you need to learn how to control so the LLM makes the right choices.
> Whenever I start working in a new code base, it takes a a non-trivial amount of time to ramp back up to full LLM productivity.
Do you find that these details translate between models? Sounds like it doesn't translate across codebases for you?
I have mostly moved away from this sort of fine-tuning approach because of experience a while ago around OpenAI's ChatGPT 3.5 and 4. Extra work on my end necessary with the older model wasn't with the new one, and sometimes counterintuitively caused worse performance by pointing it at what the way I'd do it vs the way it might have the best luck with. ESPECIALLY for the sycophantic models which will heavily index on "if you suggested that this thing might be related, I'll figure out some way to make sure it is!"
So more recently I generally stick to the "we'll handle a lot of the prompt nitty gritty" for you IDE or CLI agent stuff, but I find they still fall apart with large complex codebases and also that the tricks don't translate across codebases.
Is the non-trivial amount of time significantly less than you trying to ramp up yourself?
I am still hesitant using AI for solving problems for me. Either it hallucinates and misleads me. Or it does a great job and I worry that my ability of reasoning through complex problems with rigor will degenerate. When my ability of solving complex problems degenerated, patience diminished, attention span destroyed, I will become so reliant on a service that other entities own to perform in my daily life. Genuine question - are people comfortable with this?