guidance

To constrain LLM output and generate valid JSON, you can enforce specific grammar rules or regex patterns during text generation. This ensures the model produces deterministic, structured data, preventing formatting errors in downstream applications.

Constrained generation is a technique that forces large language models to adhere to specific formats, grammars, or selections. It solves the problem of unpredictable outputs by ensuring generated text reliably matches defined structural rules.

Yes, you can enforce regex patterns to achieve structured LLM output. This allows you to validate specific formats like dates, emails, and phone numbers directly during generation, guaranteeing the text matches your exact constraints.

You can build multi-step workflows with Pythonic control flow by applying constrained generation techniques to large language models. This enables the creation of complex conversational agents and automated processes with deterministic outputs.

Yes, constrained generation works with custom grammars for LLMs. You can apply grammar rules alongside regex and selection constraints to guarantee valid code, XML, or structured data generation.