Inference
Constrained Decoding
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema.
In depth
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema. At every step, tokens that would break the format are masked out before sampling, so invalid output cannot be produced at all. The Outlines paper showed this can be done efficiently by compiling the format into a finite-state machine and indexing the model's vocabulary against it. Provider features like OpenAI Structured Outputs and Claude strict tool use rely on the same idea, often called grammar-constrained sampling.
Primary source: arxiv.org
Example
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema.
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FAQ
Common questions
What is Constrained Decoding?
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema.
Why does Constrained Decoding matter for AI developers?
Constrained Decoding sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Constrained Decoding?
Developers Digest publishes tutorials and videos that cover Inference topics including Constrained Decoding. Check the blog and YouTube channel for hands-on walkthroughs.
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