JSON or XML Tags for LLM Output: The Format That Holds Under Pressure
Developers often struggle with Large Language Models (LLMs) generating structured output that includes conversational text, making parsing difficult. A common solution involves using XML tags as delimiters around JSON data, allowing models to include preamble or reasoning while ensuring the core data remains parsable. Alternatively, when APIs support schema enforcement during decoding, directly requesting raw JSON is more efficient and safer, especially for simpler data structures. AI
IMPACT Provides practical strategies for developers to reliably extract structured data from LLM responses, improving application robustness.