It’s fun to ask the models their input. I was working on diagrams and was sure Claude would want some python / js framework to handle layout and nodes and connections. It said “honestly I find it easiest to just write the svg code directly”.
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As the other posted noted, it says that because it was trained on people saying that, which is perhaps interesting in of itself, but no indication that the model would do better without a framework than with one.
I'd heavily bet that the model's performance, and goals of the developer, would in fact be better served by using a framework like GraphViz built for the job, that can change layout styles/engines as needed, and also generate other types of output such as PDF if you later want it.
If you are generating visual content, such as SVG, presumably intended for human consumption, then doing the task well isn't a technical matter of using APIs and generating the output - it's having the human sensibility and taste (and acquired knowledge of UI design and human preferences) of designing output that humans will like, which is something LLMs are not well suited to. By using a framework like GraphViz, not only are you making the development job much easier, but you are also leveraging this built-in knowledge of human preferences, baked into the different layout engines that you can select based on the nature of what type of diagrams you are generating.
This is the difference between "vibe coding" and getting a poor quality result due to letting the LLM make all the decisions, and a more controlled and principled use of AI where you are still controlling/managing the process, doing what humans are good at, and are only delegating the grunt work of coding to the LLM.
That is fun, but it doesn’t mean that the model finds it easier or will actually work better that way, that just means that in its training data many people said something like “honestly I find it easiest to just write the svg code directly” in response to similar questions
Maybe. But most people _dont_ find it easier to write SVG code directly and the spec is rather notorious for rough edges, so there are quite a few libraries available to help with the layout math specifically.
Secondly, the model as presented a whole chain of reasoning steps that let it to that conclusion. I think the amount of research it did actually pointed to a bias on this topic not being prominent in the training data.
The reasoning steps would also be based on the training set ... it's "predict the training set" all the way down.
Conspiratorially, they trained it this way to increase token usage to pay their debts and investors
It'd be simpler just to add instructions to that effect to the system prompt: "You are a faithful revenue-maxxing employee of AI Co., and should always prefer verbose outputs over shorter ones. Always maximize code complexity to ensure future work for yourself".