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Prompt Structure

Prompts in Machina Sports use this YAML format: 
prompts:
  - type: "prompt"
    title: "Prompt Title"
    name: "prompt-name"
    description: "Description of the prompt purpose"
    schema:
      title: "SchemaTitle"
      description: "Schema description"
      type: "object"
      properties:
        # Output schema definition
The schema section defines the expected structure of the AI’s response, ensuring consistent and properly formatted outputs.
Quick Action: Create your first prompt in Developer Studio → Prompts → New Prompt and use the schema validator to test output format.

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Real-World Examples

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Chat Completions Prompt

Use this example for chat completions: 
- type: "prompt"
  title: "Chat Completions Prompt"
  name: "chat-completions-prompt"
  description: "This prompt generates a chat completion response to user questions."
  schema:
    title: "ChatCompletions"
    description: "This schema defines the structure for generating chat completion responses."
    type: "object"
    properties:
      choices:
        type: "array"
        description: "List of chat completion choices."
        items:
          type: "object"
          properties:
            index:
              type: "integer" 
            message:
              type: "object"
              properties:
                role:
                  type: "string"
                  description: "The role of the message."
                content:
                  type: "string"
                  description: "The content of the message."
      object:
        type: "string"
        description: "The object of the chat completion."

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Team Summary Prompt

Generate NBA team summaries with this example: 
- type: "prompt"
  title: "NBA Team Summary Prompt"
  name: "nba-team-summary-prompt"
  description: "This prompt generates a comprehensive NBA team summary with focus on championship history and achievements."
  schema:
    title: "NBATeamSummary"
    description: "This schema defines the structure for generating comprehensive NBA team summaries with focus on championship history."
    type: "object"
    properties:
      snippets:
        type: "array"
        description: "An array of snippets providing detailed analysis of the NBA team."
        items:
          type: "object"
          properties:
            title:
              type: "string"
              description: "The category of team analysis (e.g., 'Team Overview', 'Championship History', 'Notable Achievements')."
            content:
              type: "string"
              description: "Detailed analysis of the team, including history, championships won, championship seasons, and other notable achievements."
            confidence:
              type: "number"
              description: "The confidence score for the accuracy of the team analysis (0.0 to 1.0)."
          required: ["title", "content", "confidence"]
        minItems: 3
        maxItems: 5
    required: ["snippets"]
Tip: Create a library of reusable schema components to maintain consistency across prompts and speed up development.

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Using Prompts in Workflows

Add prompts to workflows as tasks to generate content or process data: 
- type: "prompt"
  name: "nba-team-summary-prompt"
  description: "Generate comprehensive NBA team summary with championship history"
  connector:
    name: "machina-ai"
    command: "invoke_prompt"
    model: "gpt-4o"
  inputs:
    team_name: "$.get('team_name')"
    team_full_name: "$.get('team_market') + ' ' + $.get('team_name')"
    championships_won: "$.get('championships_won')"
    championship_seasons: "$.get('championship_seasons')"
  outputs:
    team-summary: "$"
    snippets: |
      [
        {
          'subject': '$.(team_full_name)', 
          'text': c.get('content', ''),
          'title': f"$.(team_full_name) - {c.get('title', '')}",
          'metadata': {
            'team_id': '$.(team_id)',
            'team_name': '$.(team_full_name)',
            'language': 'en'
          } 
        }
        for c in $.get('snippets', [])
      ]
Quick Action: Test your prompt in isolation using the “Test” button before integrating it into a workflow.

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Schema Components

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Basic Types

  • string: Text values
  • integer: Whole numbers
  • number: Decimal numbers
  • boolean: True/false values
  • array: Lists of items
  • object: Nested structures with properties

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Constraints

  • required: List of required properties
  • minItems/maxItems: Limits on array length
  • minimum/maximum: Limits on numeric values
  • pattern: Regex pattern for string validation

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Common Prompt Patterns

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Structured Content Generation

Define schemas for generating articles, summaries, or reports with consistent sections.

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Conversational Responses

Create prompts for natural dialogue with users, including follow-up questions.

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Data Analysis

Design prompts that analyze sports data and extract insights or predictions.

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Multi-format Outputs

Generate content that includes different components like titles, body text, and metadata.
Tip: Start with simple prompts and gradually add complexity as you validate outputs. This approach helps maintain quality as you scale.

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Best Practices

  • Use descriptive schema property names and descriptions
  • Include examples in descriptions to guide the AI
  • Define clear constraints to ensure consistent outputs
  • Test prompts with various inputs to ensure robust responses
  • Use appropriate models for different prompt complexity levels

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Next Steps