Structured Output

A post-generation validation step that verifies the output against a predefined JSON Schema or Pydantic model before passing it downstream.

• Catches semantic errors that survive constrained decoding
• Rejects outputs with missing required fields or wrong data types
• Can trigger automatic regeneration with error feedback

Example: An agent outputting {"action": "buy", "quantity": -5} fails validation because quantity must be a positive integer.

Structured Output is the foundation for tool calling and function calling in modern LLM APIs, where the model generates JSON objects that map directly to API parameters.

• OpenAI, Anthropic, and Gemini all support native JSON mode
• Parameters are defined using JSON Schema in the tool definition
• Enables deterministic agent-to-API execution without parsing ambiguity

Example: {"function": "get_weather", "parameters": {"city": "London", "unit": "celsius"}}

Unlike prompt-based formatting, true Structured Output operates at the token sampling level, not the instruction level.

• Prompt engineering alone cannot guarantee valid JSON
• Token masking modifies the logit distribution before softmax
• Provides mathematical certainty of syntactic correctness

This is the difference between asking the model to 'output JSON' and forcing every token to comply with a formal grammar.

Modern frameworks allow developers to define output structures as Pydantic models or dataclasses, which are automatically converted into grammar constraints.

• Type hints define the schema: name: str, age: int, tags: List[str]
• Nested objects and optional fields are fully supported
• Enables seamless deserialization into typed Python objects

Example: class User(BaseModel): name: str; email: EmailStr; role: Literal["admin", "user"]

When validation fails, the system can feed the error message back to the model as context for a regeneration attempt.

• Error messages like 'Field age must be an integer' guide correction
• Implements a self-healing pattern without human intervention
• Configurable max retry limits prevent infinite loops

This pattern is critical for production systems where output reliability must approach 100%.

Enforce that every LLM response conforms to a strict JSON Schema or OpenAPI specification before it leaves the server. This eliminates malformed JSON, missing required fields, and type mismatches that break downstream clients.

• Real-world impact: Stripe uses constrained generation to ensure payment intent descriptions always contain valid currency codes and amounts
• Key benefit: Eliminates the need for retry logic and defensive parsing in consuming services
• Implementation: Apply a grammar mask over invalid tokens during sampling, guaranteeing syntactic validity

Constrain agent outputs to produce precisely typed function call signatures with correct parameter names, types, and required arguments. This prevents hallucinated function names and malformed arguments that cause runtime exceptions.

• Pattern: Define a tool schema with name, description, and parameters using JSON Schema; the model must emit {"name": "search_database", "arguments": {...}}
• Safety: Combined with an Action Gate, structured tool calls prevent agents from invoking unauthorized functions
• Example: OpenAI's function calling and Anthropic's tool use both rely on constrained decoding to guarantee valid invocation syntax

Transform unstructured documents into typed knowledge graph triples or structured records by forcing the model to output only valid entity types and relationship predicates from a predefined ontology.

• Use case: Legal document processing where clauses must be extracted as {party, obligation, deadline, governing_law} tuples
• Constraint: The model's token space is restricted to only valid entity labels from the target schema, preventing hallucinated categories
• Integration: Feeds directly into Enterprise Knowledge Graphs without manual curation or fuzzy matching

Force the model to select from a closed set of labels by constraining output tokens to only valid class names. This guarantees the output is always a valid route, category, or intent.

• Pattern: Define an enum schema {"type": "string", "enum": ["billing", "technical", "account"]} and mask all other tokens
• Advantage over logit bias: Unlike post-hoc logit adjustment, constrained decoding provides a mathematical guarantee that invalid classes cannot appear
• Application: Intent routing in customer service agents where misclassification triggers incorrect workflows

Combine reasoning traces with structured final answers by using a two-phase generation: the model first produces free-form reasoning, then switches to a constrained schema for the final output.

• Technique: Use a separator token to transition from unstructured reasoning to schema-constrained generation
• Benefit: Maintains the accuracy gains from Chain-of-Thought prompting while guaranteeing parseable final outputs
• Validation: A Critic Model can verify that the structured conclusion logically follows from the reasoning trace

Generate perfectly structured training datasets by constraining outputs to match the exact schema required for downstream model fine-tuning. Every record is guaranteed valid.

• Scale: Produce millions of schema-compliant records without manual validation
• Quality: Combined with Hallucination Score filtering, only factually consistent records are retained
• Use case: Generating {question, answer, source_document} triples for fine-tuning retrieval-augmented generation models