Transforming AI Development: Introducing Structured Outputs on Amazon Bedrock

A Game-Changer for JSON Responses and Workflow Efficiency

Say Goodbye to Traditional JSON Generation Challenges

Unveiling Structured Outputs: A Paradigm Shift in AI Application Development

Key Benefits of Structured Outputs: Reliability and Efficiency

Understanding How Structured Outputs Function

Getting Started with Structured Outputs: Implementation Guide

Best Practices and Requirements for Effective Use

Practical Applications Across Industries: Real-World Use Cases

Choosing Between JSON Schema and Strict Tool Use

API Comparison: Converse vs. InvokeModel

Availability of Structured Outputs Across AWS Regions

Conclusion: Unlocking the Power of Validated JSON in AI Workflows

Meet the Authors: Experts Behind the Innovation

Transforming AI Development: Structured Outputs on Amazon Bedrock

Today, we’re thrilled to announce the introduction of structured outputs on Amazon Bedrock. This game-changing capability allows developers to extract validated JSON responses from foundation models through constrained decoding. It marks a significant shift in how AI applications are developed, setting the stage for more streamlined data pipelines and efficient workflows.

A Paradigm Shift in AI Application Development

In the past, obtaining structured data from language models required painstakingly crafted prompts and elaborate error-handling frameworks. Developers often faced a series of challenges, from parsing failures and missing fields to type mismatches and schema violations. The introduction of structured outputs eliminates these hurdles, allowing you to build zero-validation data pipelines and robust applications with confidence.

The Problems with Traditional JSON Generation

Getting reliable structured responses has been a significant pain point in AI development. Common issues include:

• Parsing Failures: Invalid JSON syntax often leads to broken json.loads() calls.
• Missing Fields: Essential data elements may be absent from responses.
• Type Mismatches: Errors occur when expected types (like integers) are returned as strings.
• Schema Violations: Responses may parse correctly yet still not adhere to your data model’s requirements.

These problems can compound, especially in a production environment where a single malformed response can trigger retries, increasing latency and costs.

What Changes with Structured Outputs

Structured outputs on Amazon Bedrock bring about not just minor improvements but a comprehensive transformation from probabilistic to deterministic outputs. Through constrained decoding, this feature ensures that model responses align with a specified JSON schema.

Two Core Mechanisms:

1. JSON Schema Output Format: Control the model’s response format, perfect for data extraction and API responses.
2. Strict Tool Use: Validate tool parameters, essential for agentic workflows and function calling.

These mechanisms can be utilized independently or in tandem, offering precise control over outputs and function calls.

Key Benefits of Structured Outputs

• Always Valid: Say goodbye to JSON parsing errors.
• Type Safe: Required fields are consistently present and properly typed.
• Reliable: No retries are needed for schema violations, promoting smoother operations.
• Production Ready: Deploy with confidence at enterprise scale.

How Structured Outputs Work

The magic behind structured outputs involves constrained sampling and compiled grammar artifacts. Here’s a breakdown of the process:

1. Schema Validation: Amazon Bedrock validates your JSON schema against the supported Draft 2020-12 subset.
2. Grammar Compilation: New schemas undergo a compilation process (the first request may take a bit longer).
3. Caching: Compiled grammars are cached for 24 hours, boosting performance for subsequent requests.
4. Constrained Generation: The model generates tokens that conform to the specified JSON schema.

Performance Considerations

• Initial Compilation: The first request may introduce latency; however, cached performance is significantly enhanced for repeated requests.
• Cache Scope: Grammars persist per account for 24 hours from first access. Changing the JSON schema structure invalidates the cache.

Getting Started with Structured Outputs

Let’s examine a practical example using the Converse API to demonstrate how structured outputs work:

import boto3
import json

# Initialize the Bedrock Runtime client
bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name="us-east-1"  # Choose your preferred region
)

# Define your JSON schema
extraction_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string", "description": "Customer name"},
        "email": {"type": "string", "description": "Customer email address"},
        "plan_interest": {"type": "string", "description": "Product plan of interest"},
        "demo_requested": {"type": "boolean", "description": "Whether a demo was requested"}
    },
    "required": ["name", "email", "plan_interest", "demo_requested"],
    "additionalProperties": False
}

# Make the request with structured outputs
response = bedrock_runtime.converse(
    modelId="us.anthropic.claude-opus-4-5-20251101-v1:0",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
                }
            ]
        }
    ],
    inferenceConfig={
        "maxTokens": 1024
    },
    outputConfig={
        "textFormat": {
            "type": "json_schema",
            "structure": {
                "jsonSchema": {
                    "schema": json.dumps(extraction_schema),
                    "name": "lead_extraction",
                    "description": "Extract lead information from customer emails"
                }
            }
        }
    }
)

# Parse the schema-compliant JSON response
result = json.loads(response["output"]["message"]["content"][0]["text"])
print(json.dumps(result, indent=2))

Expected Output:

{
  "name": "John Smith",
  "email": "john@example.com",
  "plan_interest": "Enterprise",
  "demo_requested": true
}

The response conforms to the specified schema, requiring no additional validation steps.

Requirements and Best Practices

To maximize the effectiveness of structured outputs, consider the following guidelines:

1. Set additionalProperties: false: This is crucial for your schema to be accepted.
2. Use Descriptive Names: Clear field names and descriptions enhance understanding.
3. Implement enum for Constrained Values: This ensures accuracy in specified fields.
4. Start Simple and Scale Gradually: Begin with essential fields before adding complexity.
5. Reuse Schemas: Efficiently leverage cached schemas for performance boosts.

Practical Applications Across Industries

Structured outputs have significant implications across various sectors:

• Financial Services: Extract structured data from documents while ensuring completeness and correctness.
• Healthcare: Parse clinical notes into validated records for EHR systems.
• Ecommerce: Streamline product catalog pipelines for reliable data extraction.
• Legal: Analyze contracts for standardized data extraction.
• Customer Service: Construct intelligent systems that extract and match intents with application models.

Conclusion

Structured outputs on Amazon Bedrock redefine the way we work with AI-generated JSON. By ensuring validated, schema-compliant responses, developers can build robust data pipelines, reliable workflows, and scalable applications—all without the intricacies of custom validation logic.

This exciting feature is now generally available on Amazon Bedrock. Equip yourself with the latest AWS SDK and explore the future of AI application development today!

What innovative workflows could validated, schema-compliant JSON unlock in your organization? Dive into the sample notebook and discover the possibilities.

About the Authors

Jeffrey Zeng is a Worldwide Specialist Solutions Architect for Generative AI at AWS, focused on helping customers deploy AI solutions from concept to production.

Jonathan Evans is a Worldwide Solutions Architect for Generative AI at AWS, specializing in leveraging cutting-edge AI technologies to solve complex business challenges.