Structured Decoding
Structured decoding is an inference-time technique that constrains the model's output to conform to a grammar, regular expression, or JSON schema by masking invalid tokens at each generation step. Because the constraint is enforced during sampling rather than hoped for via prompt instructions, the output is syntactically valid by construction — no parsing retries, no regex cleanup, no hallucinated fields. It is distinct from "structured output" as a prompt-level instruction, which asks the model to comply but cannot guarantee it. Popular libraries include Outlines, Guidance, and XGrammar. The trade-off is a small per-token overhead for the mask computation and the need to express the target shape as a formal grammar.
Example
An extraction pipeline needs every response to match a JSON schema with four required fields and enumerated values for two of them. The team switches from prompt-level "return JSON" instructions to structured decoding with the schema compiled to a grammar. The parsing-failure rate drops from 3% to 0%, retry cost vanishes, and downstream code no longer needs fallback parsing branches.
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