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config-structured-outputs (Multi-Provider Structured Outputs)
You can run this example with:
npx promptfoo@latest init --example config-structured-outputs
cd config-structured-outputs
This example demonstrates how to enforce structured JSON outputs using schema validation across multiple AI providers:
- OpenAI - using
response_formatwithjson_schema - Azure OpenAI - using
response_formatwithjson_schema - Anthropic - using
output_formatwithjson_schema
The example includes two prompt configurations for solving quirky math problems:
- One that requires step-by-step problem solving (with
stepsarray) - One that only requires the final answer (without
stepsarray)
Environment Variables
This example requires at least one of the following API keys:
OPENAI_API_KEY- Your OpenAI API key for testing with GPT modelsANTHROPIC_API_KEY- Your Anthropic API key for testing with Claude models- For Azure OpenAI: See Azure OpenAI provider docs for required credentials
You can set these in a .env file or directly in your environment:
export OPENAI_API_KEY=your_api_key_here
export ANTHROPIC_API_KEY=your_api_key_here
Provider Differences
OpenAI/Azure Format
OpenAI and Azure use response_format at the prompt level:
prompts:
- raw: 'Your prompt here'
config:
response_format:
type: json_schema
json_schema:
name: schema_name
strict: true
schema:
# Your schema here
Anthropic Format
Anthropic uses output_format at the provider level:
providers:
- id: anthropic:messages:claude-sonnet-4-6
config:
output_format:
type: json_schema
schema:
# Your schema here (no nested json_schema object)
Getting Started
-
Review and customize
promptfooconfig.yamlas needed -
Remove any providers you don't have API keys for
-
Run the evaluation:
promptfoo eval -
View the results:
promptfoo view
What You'll Learn
- How to enforce JSON schemas across different providers
- The syntax differences between OpenAI and Anthropic structured outputs
- How to validate that responses are always valid JSON objects
- How to require specific fields and types in LLM responses