guidance

Constrained generation uses regex and grammar constraints to guarantee structured LLM outputs always match your specified JSON, XML, or code formats. This mechanism forces the model to select only tokens that comply with your defined syntactic rules during inference.

Constrained generation applies regex and grammar constraints to guarantee structured LLM outputs always match your specified JSON, XML, or code formats. This mechanism forces the model to select only tokens that comply with your defined syntactic rules during inference.

Regex constraints enforce valid LLM outputs by applying pattern rules during generation to guarantee format correctness for specific fields like emails, dates, or IDs. This intercepts invalid tokens before they are generated, ensuring compliance without post-processing.

Build multi-step workflows for structured data generation using Pythonic control flow and reusable guidance functions. This chains constrained generation steps together to produce complex, valid JSON or XML structures across sequential LLM calls.

Applying grammar constraints for structured output requires the guidance and transformers libraries as dependencies. These provide the necessary environment to enforce regex rules and constrained selection directly during the LLM generation process.

LLMs produce invalid JSON because standard generation lacks syntactic enforcement. Applying grammar constraints during constrained generation guarantees valid JSON by forcing token selection to match your schema, eliminating unpredictable formatting and malformed outputs.