Guide shows how to build a fast LLM decision model by constraining outputs to fixed answer choices

The article explains “system one” decision models that choose from predefined options in a single inference pass, rather than generating multi-token structured output step by step. It shows how an LLM such as Qwen can be constrained by masking the vocabulary so only allowed answers like A through E can be emitted.

This approach highlights a growing interest in LLM classification workflows that prioritize speed, determinism, and simple integration over open-ended generation. However, token probabilities should not be treated as reliable confidence scores without calibration or additional training, limiting their usefulness for high-stakes decisions.