llm-structured-output

LLM Structured Output

Build structured-output flows that are predictable enough for software to trust.

Use This Skill For

• JSON or typed-object output from a model
• Data extraction pipelines
• Function or tool argument generation
• Prompt contracts that feed downstream automation
• Validation and retry strategies for malformed model output

Workflow

1. Start With the Consumer

• Identify exactly what the downstream system needs
• Define required fields, optional fields, enums, and limits
• Keep the schema as small as possible

2. Make the Contract Explicit

• Provide the model with the expected structure
• State field meanings and constraints clearly
• Prefer deterministic formats over prose-plus-JSON hybrids
• If a field is free-form, bound it with type, length, or examples

3. Validate Everything

• Parse strictly
• Reject unknown or malformed shapes when correctness matters
• Validate enums, ranges, array sizes, and nested objects
• Treat structured output as untrusted input until validated

4. Design Recovery Paths

• Retry with the validation error when the output is close
• Fall back to a smaller schema if the original is too complex
• Log invalid outputs for inspection
• Avoid silent coercion that hides model mistakes

5. Optimize for Reliability

• Break large tasks into smaller structured steps
• Separate reasoning from final machine-readable output when needed
• Prefer schemas with stable keys and low ambiguity
• Remove optional fields that are not actually useful

Rules

• Do not ask for markdown fences around JSON unless the consumer needs them
• Do not mix human-facing commentary into machine-facing payloads
• Prefer enums over natural-language categories
• Prefer arrays of objects over encoded strings
• Make nullability intentional, not accidental

Common Failure Modes

• Schema too broad for the task
• Required fields that the model cannot infer
• Free-text values that should be enums
• Nested output with no examples or constraints
• Parsers that accept bad data and fail later in the pipeline

Output

When using this skill, produce:

• The target schema or shape
• The prompt contract for generating it
• The validation and retry plan
• Any reliability risks or edge cases