Yeah, I hear you. I think you see using LLMs for parsing only as intuitive and obvious, but I bet most devs don't think of doing it that way, at least not at fi

Yeah, I hear you. I think you see using LLMs for parsing only as intuitive and obvious, but I bet most devs don't think of doing it that way, at least not at first. After all, the thing is right there, willing to talk to you, and the most obvious way to use them is to take the words generated and send them to the customer. Using LLMS to generate tool invocations that then generate templated words is a much more indirect use, and frankly up until recently is not always practical due to the amount of context window that all the tool descriptions and possible actions can take up (which must fit not only the 'training' for the model but also the customer's conversation so far). As tool use becomes more widespread and context window sizes go up, I bet we'll start to see blog posts where devs talk about how they made your approach work well, and then others will see it makes sense and start to copy it.