Constrained generation is a natural language processing technique that guides language models to produce text adhering to specific rules, enhancing the coherence and reliability of outputs, particularly in structured tasks like generating formatted documents or JSON data. This method is demonstrated in reasoning models such as DeepSeek R1, where structured outputs are produced through constrained decoding, simplifying the prediction process and improving efficiency. The Fireworks AI platform utilizes constrained generation to offer applications in areas like structured Q&A, healthcare record generation, and computer system specifications, ensuring outputs are precise and machine-readable. By employing JSON modes and grammar-based constraints, the models deliver transparent and consistent results, integrating seamlessly into real-world systems. Fireworks AI further supports these capabilities with its enterprise-scale inference engine, providing low-latency and high-throughput performance, making it a valuable tool for developers building generative AI applications.