[goalieca]

> I tell it to validate each parameter against my version moving forward and it clearly doesn't do that.

I would like an AI expert to weigh in on this point. I run into this a lot. It seems that LLMs, being language models and all, don't actually understand what i'm asking. Whenever i dive into the math, superficially, it kind of makes sense why they don't. But it also seems like transformers or some secret sauce is code up specially for tasks like counting letters in a word so that AI doesn't seem embarrassing. Am I missing something?

[nerdponx]

LLMs are next-token prediction models. They "understand" in that, if the previous 1000 tokens are such-and-such, then they emit a best guess at the 1001th token.

It "knows" what rsync is because it has a lot of material about rsync in the training data. However it has no idea about that particular version because it doesn't have much training data where the actual version is stated, and differences are elaborated.

What would probably produce a much better result if you included the man page for the specific version you have on your system. Then you're not relying on the model having "memorized" the relationship relationships among the specific tokens you are trying to get the model to focus on, instead just passing it all in as part of the input sequence to be completed.

It is absolutely astounding that LLMs work at all, but they're not magic, and some understanding of how they actually work can be helpful when it comes to using them effectively.

[pglevy]

Our low-code expression language is not well-represented in the pre-training data. So as a baseline we get lots of syntax errors and really bad-looking UIs. But we're getting much better results by setting up our design system documentation as an MCP server. Our docs include curated guidance and code samples, so when the LLM uses the server, it's able to more competently search for things and call the relevant tools. With this small but high-quality dataset, it also looks better than some of our experiments with fine tuning. I imagine this could work for other docs use cases that are more dynamic (ie, we're actively updating the docs so having the LLM call APIs for what it needs seems more appropriate than a static RAG setup).

[bashy]

Words are tokens so it can't really 'see' the word(s), it just knows how to link them.

[beefnugs]

did you feed the context a link or examples of the exact version of the documentation?

I am not an expert but i assume if there are any basic knowledge of the tool then it will try to use it as it knows chunks of some old version. And it wont likely decide to search for the newest docs, you have to tell it search for exact version docs, or feed the exact version docs to context

[goalieca]

I find context _sometimes_ work but more often than not i rephrase the question a million times, try to break it down into smaller problems, .. whatever. It seems like it just doesn't "understand".