Constrained Decoding and Grammars

Constrained decoding and grammar-based generation are critical techniques for building reliable, predictable LLM systems. Rather than letting models generate free-form text, you can enforce hard structural constraints—JSON shape, regex patterns, finite-state sequences—to guarantee output parsability and reduce hallucination. This 10-article series takes you from foundational concepts (what constrained decoding is, how logit masking works) through production-grade tooling (Outlines, XGrammar, llama.cpp integration) and real-world reliability patterns.

Whether you're building form-filling agents, API integrators, or deterministic code generators, constrained decoding eliminates parse failures and post-processing errors. By learning formal grammar notation (GBNF), understanding the trade-offs between constraint expressiveness and generation speed, and mastering libraries like Outlines, you'll build systems that consistently produce valid, usable structured output—even under adversarial prompts or ambiguous instructions.

Each article is self-contained but builds on prior topics. Start with the fundamentals, then specialize in whichever constraint mechanism fits your use case (JSON grammars for APIs, regex for pattern matching, finite-state machines for sequential workflows). The final article synthesizes constraints with prompt-engineering best practices to show how to combine hard guarantees with natural-language guidance for maximum reliability.

Articles in this series​

Articles in this series