guidance

To enforce structured output and valid JSON generation from an LLM, use constrained generation with regex or grammars. This ensures the syntax remains valid, preventing formatting errors during structured data generation tasks.

The best way to build multi-step workflows for constrained LLM generation is using Pythonic control flow with grammatical constraints. This approach prevents invalid intermediate outputs across sequential generation steps.

Yes, you can use regex to control LLM output syntax for specific formats like emails, dates, and IDs. This constrained generation technique strictly enforces structured formats during model inference.

Yes, you need the transformers library along with guidance and llama.cpp dependencies to use this framework. These libraries provide the underlying environment for executing constrained generation workflows.

To automate generating structured product listings from unstructured text, apply multi-step workflows with grammatical constraints. This transforms raw text into valid, structured records through Pythonic control flow.