guidance

Structured generation forces language models to produce valid JSON, XML, or code by applying regex and grammar constraints, ensuring outputs conform to exact syntax requirements without manual cleanup.

Token healing is a constrained generation technique that fixes boundary artifacts during text generation. You need it when enforcing strict syntax rules to prevent partial tokens from corrupting your structured output format.

Yes, constrained generation supports selectable model backends including local runtimes like llama.cpp and Transformers, as well as API providers like Anthropic and OpenAI, for consistent structured output across environments.

You build multi-step agent workflows by orchestrating tool-using agents with reusable Python functions, allowing you to chain prompts and validation-heavy extraction steps into reliable automated pipelines.

The best way to extract clean records is using structured generation with selection rules and grammar constraints, turning the LLM into a reliable data extractor that returns clean records for downstream automation.

LLMs return malformed JSON when free-form generation lacks syntax enforcement. Applying grammar constraints and structured generation rules forces the model to produce valid, predictable text that conforms to your exact schema.