“You want to extract some information from unstructured data, use an extraction model. Then you want to classify what it finds. Then you use a decision model. And then if you want to reinterpret or summarize that for the user, you then pass that into an LLM. So you can create a chain of these models that work together instead of using the LLM to just do everything that is more prone to failure.”

Exactly. But you're never going to be guaranteed that it's going to give it back to you exactly the same way every time and in the right format. With an extraction model, that's guaranteed. You will always get the schema back that you expect. Now, whether the probabilities are going to be the same, that's where they share a little bit of similarity with the probability, right? Where it's still making a probabilistic guess. So it's still guessing, but the percentage is baked into the output as something that's still deterministic that you can then take action on. So the idea, yeah, the idea here is that these all kind of work together, right? Like you want to extract some information from unstructured data, use an extraction model. Then you want to classify what it finds. Then you use Jev or like a decision model. And then if you want to reinterpret or summarize. That for the user, you then pass that into an LLM, right? So you can create a chain of these models that work together instead of using the LLM to just do everything that is more prone to failure.