Large Language Models (LLMs) are exceptionally good at generating text. We use them to answer questions, write code, reason about problems, and so much more. But not every problem requires generating text.
The system needs to determine which team should handle the request, how urgent it is, and whether the customer is requesting a refund.
But what if the model could directly evaluate the available options and return a probability distribution over them, without generating any text?
A decision model takes some input context (state), evaluates a question with a set of predefined possible answers (primitives), and returns a structured decision along with probabilities. Instead of asking a model to generate an answer token by token, we ask it to assign probabilities to the answers that our application already understands.
