Comment by sleples
11 hours ago
We've gone from "you're holding it wrong" to "the training data was bad because humans suck too". Difference is, humans learn from their mistakes.
11 hours ago
We've gone from "you're holding it wrong" to "the training data was bad because humans suck too". Difference is, humans learn from their mistakes.
A singular human does (or tends to). Humans as a group, where members join and leave a group with time, also do learn, but at a much slower pace - over the years to decades timeframe. "X things programmers should know about Y" is a template for quite a few very influential blog posts, yet for most of them, you find many programmers, even decades later, who don't actually know what they "should".
My experience was always that 90% of code is ugly and clunky. I'm not at all surprised, while reviewing AI-generated code, to see many of the same ugliness we regularly commit. The quality of the output code is now consistently average, which means it's basically shit in 90% of cases, but it tends to mostly work (in the general case). The same kind of shit I've seen people push to production thousands of times in my career.
We don't fully know how to write good code. We don't really understand what good code should objectively look like. Spending more time on code doesn't automatically lead to better code (but costs a lot more). Above all, we don't need good code - the business side is perfectly fine with "good enough right now" rather than "maybe a lot better half a year from now". And that's what the models are trained on. They would, indeed, need quite a lot of "emergent properties" to go from that to consistently good code. ASI-level properties, I suspect.
> Difference is, humans learn from their mistakes.
Great! So next time the human will prompt the agent to watch out for and avoid this bug.
Given the amount of training data out there, LLMs should have been perfect by now.
> Great! So next time the human will prompt the agent to watch out for and avoid this bug.
I actually created a system for something like this. The basic idea is, once you have identified what the issue was and fixed it, you can create lessons that lives inside the repository. Lessons are designed to be mapped to one or more files so if the LLM changes the files again, they can see what the issue was.
The main challenge is being able to summarize and create proper tags so the AI after any code change can easily find the lesson.
You are a senior developer. Please do no mistakes!