Generating Structured Outputs from LLMs

The article discusses various techniques for constraining large language models (LLMs) to produce outputs that adhere to predefined schemas, enhancing their reliability and applicability in structured tasks. Key methods include prompt engineering, fine-tuning, and the use of specialized decoding strategies such as constrained decoding and output templates, which collectively improve the consistency and validity of LLM-generated data within specific frameworks.