guidance

To guarantee valid JSON output from an LLM without retry loops, apply constrained generation to enforce token-level regex and grammar constraints. This ensures models produce valid structured formats like JSON, XML, and code deterministically.

Yes, you can enforce regex patterns for specific fields during structured data extraction. By applying context-free grammars and token-level constraints, the model generates values that match specific formats like emails, dates, and UUIDs.

Constrained generation works with both local models and API backends. It is compatible with Anthropic, OpenAI, Transformers, and llama.cpp, allowing you to enforce output syntax across different platforms.

Token healing is a technique that avoids token-boundary artifacts during generation. You need it when enforcing structured outputs to ensure deterministic choice selections and prevent formatting errors caused by tokenization mismatches.

Build multi-step ReAct agents with validated tool inputs by creating Pythonic, stateful or stateless generation functions. Constrained generation ensures the agent produces valid syntax for tool calls and API response formatting.

The best way to format API responses deterministically is to apply constrained generation using grammar constraints and selection choices. This eliminates unreliable free-form outputs and enforces strict structural formats without expensive retries.