Comment by enriquto
21 days ago
This steering is the main "source code" of the program that you wrote, isn't it? Why throw it away. It's like deleting the .c once you have obtained the .exe
21 days ago
This steering is the main "source code" of the program that you wrote, isn't it? Why throw it away. It's like deleting the .c once you have obtained the .exe
It's more noise than signal because it's disorganized, and hard to glean value from it (speaking from experience).
I wasn’t exactly suggesting this. The source code (including SVG or DOCX or HTMl+JS for document work) is the primary ground truth which the LLM modifies. Humans might modify it too. This ground truth is then rendered (compiled, visualized) to the end product.
The PROMPTS.md is communication metadata. Indeed, if you fed the same series of prompts freshly, the resultant ground truths might not make sense because of the stochastic nature of LLMs.
Maybe “ground truth” isn’t exactly the right word, but it is the consistent, determined basis which formed from past work and will evolve with future work.
> because of the stochastic nature of LLMs.
But is this "stochastic nature" inherent to the LLM? Can't you make the outputs deterministic by specifying a version of the weights and a seed for the random number generator?
Your vibe coding log (i.e. your source code) may start like this:
Notice that the first two lines may be added automatically by the system and you don't need to write or even see them.
I see what you are saying, and perhaps we are zeroing in on the importance of ground truths (even if it is not code but rather PLANs or other docs).
For what you're saying to work, then the LLM must adhere consistently to that initial prompt. Different LLMs and the same LLM on different runs might have different adherence and how does it evolve from there? Meaning at playback of prompt #33, will the ground truth gonna be the same and the next result the same as in the first attempt?
If this is local LLM and we control all the context, then we can control that LLM's seeds and thus get consistent output. So I think your idea would work well there.
I've not started keeping thinking traces, as I'm mostly interested in how humans are using this tech. But, that could get involved in this as well, helping other LLMs understand what happened with a project up to a state.
> But is this "stochastic nature" inherent to the LLM?
At any kind of reasonable scale, yes. CUDA accelerators, like most distributed systems, are nondeterministic, even at zero temperature (which you don't want) with fixed seed.