Generating Structured Outputs from LLMs

Generating structured output from Large Language Models is essential for integrating them into software applications that require data in a predefined schema. The primary methods to achieve this include relying on API provider features, such as those from OpenAI or Google, which offer simplicity at the cost of flexibility. A second technique involves prompting and reprompting, where the model is instructed to follow a structure and the system retries if the output fails parsing, a process facilitated by libraries like Instructor. A more advanced and reliable approach is constrained decoding, which modifies the token generation process using computational linguistics concepts like regular expressions and finite automata to guarantee the output always conforms to the required schema without needing retries.