Without structured outputs, for some classification tasks you can use the hallucinate -> resolve pattern

Step one ask the LLM to classify something from the prompt “creatively”. For example, ask it to classify the color or category of a product in an e-commerce catalog or user request. Give examples of what valid instance of these entities look like, ask for output that looks like these examples (encourage the LLM to engage in creative hallucination). Often helps to get the LLM to pick more than one and for it to choose many different, realistic diverse labels.

Step two, with hallucinated entities, lookup via embedding similarity to find the most similar “real” entities. Then return these.

It can save you a lot of tokens (you don’t have to enumerate every legal value). And you can get by with a tiny model.