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This article details practical constraint decoding techniques that use finite state machines and logit masking to mathematically enforce specific output schemas on large language models during inference. By filtering invalid tokens before generation rather than relying on post-processing or prompt engineering, this approach ensures strict adherence to data structures like JSON without model hallucination.

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An Introductory Guide to Practical Constraint Decoding

KDnuggetsJuly 28, 20264 min read

With this introductory guide to practical constraint decoding, you'll no longer need to beg your model to "output valid JSON without including any markdown."

Practical constraint decoding, also known as structured generation or guided decoding, encompasses the engineering strategies to force a large language model (LLM) to generate text outputs that strictly abide by a specified data schema, grammar, or regular expression (regex) at the token selection stage. With the introductory guide to practical constraint decoding in this article, you'll no longer need to beg your model to "output valid JSON without including any markdown", just to cite an…

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