Comment by motorest
4 days ago
> Good architecture plans help.
This is they key answer right here.
LLMs are great at interpolating and extrapolating based on context. Interpolating is far less error-prone. The problem with interpolating is that you need to start with accurate points so that interpolating between them leads to expected and relatively accurate estimates.
What we are seeing is the result of developers being oblivious to higher-level aspects of coding, such as software architecture, proper naming conventions, disciplined choice of dependencies and dependency management, and even best practices. Even basic requirements-gathering.
Their own personal experience is limited to diving into existing code bases and patching them here and there. They often screw up the existing software architecture because their lack of insight and awareness leads them to post PRs that get the job done at the expense of polluting the whole codebase into an unmanageable mess.
So these developers crack open an LLM and prompt it to generate code. They use their insights and personal experience to guide their prompts. Their experience reflects what they do on a daily basis. The LLMs of course generate code from their prompts, and the result is underwhelming. Garbage-in, garbage-out.
It's the LLMs fault, right? All the vibe coders out there showcasing good results must be frauds.
The telltale sign of how poor these developers are is how they dump the responsibility of they failing to get LLMs to generate acceptable results on the models not being good enough. The same models that are proven effective at creating whole projects from scratch at their hands are incapable of the smallest changes. It's weird how that sounds, right? If only the models were better... Better at what? At navigating through your input to achieve things that others already achieve? That's certainly the model's fault, isn't it?
A bad workman always blames his tools.
Yes, with a bit of work around prompting and focusing on closed context, or as you put it, interpolating, you can get further. But the problems is that, this is not how the LLMs were sold. If you blame someone for trying to use it by specifying fairly high level prompts - well isn´t that exactly how this technology was being advertised the whole time? The problem is not the bad workman, the problem is that the tool is not doing what it is advertised as doing.
> But the problems is that, this is not how the LLMs were sold.
No one cares about promises. The only thing that matters are the tangibles we have right now.
Right now we have a class of tools that help us write multidisciplinary apps with a few well-crafted prompts and zero code involved.