guidance

To constrain LLM outputs for valid JSON, you apply grammar-based generation rules that enforce specific schemas and structures. This ensures the model produces syntactically correct JSON, reducing hallucinations and guaranteeing deterministic results for data extraction workflows.

Token healing fixes tokenization artifacts at prompt boundaries during constrained generation. It improves coherence and reliability in complex multi-step workflows by ensuring smooth transitions between prompt segments and structured outputs.

Yes, you can use regex-based constraints to validate specific data formats like emails, dates, and IDs during generation. This forces the model to produce text strictly matching your defined regular expressions, ensuring format compliance.

Constrained generation via grammar and regex rules supports both local and API backends. This allows you to enforce structured formats and reusable primitives across diverse software engineering and data analytics environments without provider lock-in.

Use grammar constraints instead of standard prompt engineering when you need guaranteed valid structures like XML or domain-specific languages. Grammar constraints enforce deterministic formatting at the generation level, whereas prompt engineering relies on probabilistic compliance.