structured-generation

Schema validation for LLM outputs works by constraining responses via API-enforced schemas when available, or using schema-in-prompt fallbacks with robust post-parse validation and retry logic to ensure predictable data shapes.

LLM data extraction returns inconsistent text because models produce free-form output. Applying structured output validation enforces stable schemas, making downstream parsing reliable for pipelines and form filling.

The best way to validate structured output is layering API-enforced schemas for strict adherence, prompt-based schemas as a fallback, and post-parse validation with retry logic to recover from formatting failures.

Yes, you can use prompt-based schemas when API-enforced typing is unavailable. This Skill supports schema-in-prompt constraints as a fallback, combined with post-parse validation to guarantee machine-readable results.

To handle LLM responses that fail post-parse validation, this Skill implements robust retry logic, automatically re-prompting the model to correct formatting errors and produce outputs matching the defined schema.