Table of Contents

What is Structured Output?


TL;DR

Structured output is the ability to constrain a Large Language Model's generation so that it produces valid, machine-readable data (JSON, XML, typed objects, enums) rather than free-form natural language. This is critical for any application where LLM output must be parsed, stored, or consumed by downstream code. Without constraints, models may produce syntactically invalid JSON, include extra commentary, or deviate from the expected schema. LM-Kit.NET solves this through grammar-constrained decoding, which enforces output validity at the token level during sampling, guaranteeing that every generated response conforms to the specified format.


What Exactly is Structured Output?

Language models generate text one token at a time by sampling from a probability distribution over the vocabulary. By default, any token can follow any other token, which means the model might produce:

Here is the data you requested:

{
  "name": "Alice",
  "age": 30,
  // Note: I added a comment for clarity
  "email": "alice@example.com"
}

I hope this helps! Let me know if you need anything else.

This response is conversationally helpful but catastrophic for automation: the JSON is wrapped in commentary, contains an invalid comment, and would break any JSON parser trying to consume it.

Structured output eliminates this problem by ensuring the model can only produce tokens that result in valid output according to a predefined schema or grammar. The model's creativity is preserved within the schema's constraints: it decides what data to generate, but the structure is guaranteed.

The Reliability Problem

Without structured output, integrating LLMs into software systems requires fragile workarounds:

Approach Problem
"Please respond in JSON only" (prompt instruction) Models ignore instructions unpredictably
Regex extraction from free text Brittle; breaks on format variations
Retry on parse failure Wasteful; no guarantee of convergence
Post-processing and repair Complex; may alter the model's intent

Structured output solves this at the generation level, not the parsing level. Invalid tokens are never produced in the first place.


Why Structured Output Matters

  1. Reliable Automation: When an LLM's output feeds into a database, API call, or UI component, the output must be valid every time, not just most of the time. A 95% success rate means 1 in 20 requests fails, which is unacceptable in production.

  2. Type Safety: In strongly-typed languages like C#, structured output enables direct deserialization into typed objects. The model fills in the data; the schema guarantees the shape.

  3. Function Calling: Function calling depends entirely on structured output. When an agent decides to invoke a tool, the arguments must be valid JSON matching the tool's parameter schema. See AI Agent Tools.

  4. Data Extraction: Extracting structured data from unstructured text (invoices, contracts, emails) requires the model to produce output matching a predefined schema. See Structured Data Extraction and Extraction.

  5. Multi-Agent Communication: In compound AI systems, agents communicate by passing structured data between pipeline stages. Invalid output from one agent breaks the entire workflow.

  6. Smaller Model Enablement: Larger models follow format instructions more reliably, but even they fail occasionally. Grammar constraints allow smaller models to produce perfectly structured output, enabling cost-effective deployments that would otherwise require expensive large models.


Technical Insights

How Structured Output Works

Grammar-Constrained Decoding

The most robust approach to structured output. At each generation step, a grammar (typically defined as a context-free grammar, JSON schema, or regular expression) determines which tokens are valid continuations. Invalid tokens have their logits set to negative infinity before sampling, making them impossible to select:

Schema: { "name": string, "age": integer }

Step 1: Model must output '{'          → Only '{' token allowed
Step 2: Model must output '"name"'     → Only '"' token allowed
Step 3: Model must output ':'          → ...
Step 4: Model generates string value   → Any string tokens allowed
Step 5: Model must output ','          → Only ',' or '}' allowed
...
Final: Model must output '}'           → Guaranteed valid JSON

The model retains full freedom to choose content (which name, which age) while the grammar enforces structure (valid JSON, correct field names, correct types).

LM-Kit.NET implements this through grammar sampling, which operates at the sampling layer of the inference pipeline. See the Enforce Structured Output with Grammar guide for implementation details.

JSON Schema Enforcement

A JSON schema defines the expected structure, field names, types, and constraints. The grammar is automatically derived from the schema:

{
  "type": "object",
  "properties": {
    "sentiment": { "type": "string", "enum": ["positive", "negative", "neutral"] },
    "confidence": { "type": "number", "minimum": 0, "maximum": 1 },
    "keywords": { "type": "array", "items": { "type": "string" } }
  },
  "required": ["sentiment", "confidence"]
}

With this schema enforced, the model cannot output an invalid sentiment value, a non-numeric confidence, or a malformed array. Every output is guaranteed to be schema-compliant.

Enum and Classification Constraints

For classification tasks, structured output can restrict the model to a fixed set of valid labels:

Allowed values: ["bug", "feature", "question", "documentation"]

Model can ONLY output one of these four strings.

This is far more reliable than prompting "choose from the following options" and hoping the model complies.

Structured Output vs. Prompt Engineering

Aspect Prompt Engineering Structured Output
Mechanism Instructions in natural language Constraints at the token level
Reliability Model may ignore instructions 100% guaranteed compliance
Overhead None Minimal (grammar checking per token)
Flexibility Any format describable in text Formats expressible as grammars
Model size dependency Larger models comply more often Works reliably with any model size
Debugging Unpredictable failures Deterministic structure

In practice, the best results come from combining both: prompt instructions guide the model's content choices while grammar constraints guarantee the format.

Levels of Structure

Structured output exists on a spectrum:

Level 1: Format Compliance The output is valid JSON/XML/YAML, but no schema is enforced.

Level 2: Schema Compliance The output matches a specific schema with required fields, types, and constraints.

Level 3: Semantic Compliance The output is schema-valid and the values are semantically correct (e.g., a "date" field contains an actual date, not just any string). This level combines grammar constraints with the model's understanding.

Level 4: Validated Compliance Schema-valid output that has been verified against external sources or business rules. This is where extraction validation comes in.


Practical Use Cases

  • API Integration: An LLM generates API request bodies that must conform to a specific JSON schema. Grammar constraints guarantee valid requests, eliminating retry loops. See Build an Agent that Calls REST APIs.

  • Data Extraction from Documents: Extract structured fields (names, dates, amounts, addresses) from invoices, contracts, or forms. See the Invoice Data Extraction demo and the Extract Structured Data guide.

  • Classification Pipelines: Force the model to output exactly one category label from a predefined set, enabling reliable automated classification. See Classification.

  • Tool and Function Calling: When an agent invokes a tool, the arguments must be valid JSON matching the tool's parameter schema. Structured output makes function calling reliable even with smaller models.

  • Configuration Generation: An agent generates configuration files (JSON, YAML) that must be syntactically valid and conform to application-specific schemas.

  • Survey and Form Responses: Constrain LLM output to match expected form field types: enums for multiple choice, numbers for ratings, strings with length limits for text fields.

  • Multi-Agent Pipelines: In orchestrated workflows, agents pass structured data between stages. Grammar constraints ensure each stage receives valid input from the previous one.


Key Terms

  • Structured Output: LLM-generated text that conforms to a predefined format or schema, enabling reliable machine parsing.

  • Grammar-Constrained Decoding: A technique that restricts token generation to only those tokens that produce output valid according to a formal grammar. See Grammar Sampling.

  • JSON Schema: A declarative format for describing the structure, types, and constraints of JSON data. Used as the specification for structured output generation.

  • Logit Masking: Setting the probability of invalid tokens to zero (negative infinity logits) before sampling, preventing the model from ever selecting them. See Logits.

  • Schema Compliance: The property of output conforming exactly to a predefined schema, including field names, types, required fields, and value constraints.

  • Context-Free Grammar (CFG): A formal grammar that defines the set of valid output strings. JSON, XML, and most structured formats can be expressed as CFGs.

  • Constrained Decoding: The broader category of techniques that restrict what a model can generate, of which grammar-constrained decoding is the most powerful form.





External Resources


Summary

Structured output transforms language models from conversational text generators into reliable data producers. By enforcing format constraints at the token sampling level through grammar-constrained decoding, structured output guarantees that every model response conforms to the expected schema, whether JSON, XML, enum values, or typed objects. This is foundational for production AI systems: function calling depends on valid tool arguments, data extraction requires schema-compliant output, classification needs constrained label sets, and multi-agent pipelines require structured inter-agent communication. Combined with prompt engineering to guide content and grammar constraints to guarantee format, structured output makes LLM integration robust, predictable, and suitable for enterprise automation.

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