Prompts

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.

Real-World Examples

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."

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.

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.

Schema Components

Basic Types

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

Constraints

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

Common Prompt Patterns

Structured Content Generation

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

Conversational Responses

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

Data Analysis

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

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.

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

Next Steps

Build a Workflow to orchestrate your prompts
Connect Data Sources to feed your prompts
Deploy an Agent that uses your prompts
Test and Refine your prompts with real data

Machina Studio

Quickstart Guides

Core Components

Agents

MCP

Ecosystem

Machina SDK

Agent Templates

Pricing & Plans

Prompt Structure

Real-World Examples

Chat Completions Prompt

Team Summary Prompt

Using Prompts in Workflows

Schema Components

Basic Types

Constraints

Common Prompt Patterns

Structured Content Generation

Conversational Responses

Data Analysis

Multi-format Outputs

Best Practices

Next Steps

​Prompt Structure

​Real-World Examples

​Chat Completions Prompt

​Team Summary Prompt

​Using Prompts in Workflows

​Schema Components

​Basic Types

​Constraints

​Common Prompt Patterns

​Structured Content Generation

​Conversational Responses

​Data Analysis

​Multi-format Outputs

​Best Practices

​Next Steps

Prompt Structure

Real-World Examples

Chat Completions Prompt

Team Summary Prompt

Using Prompts in Workflows

Schema Components

Basic Types

Constraints

Common Prompt Patterns

Structured Content Generation

Conversational Responses

Data Analysis

Multi-format Outputs

Best Practices

Next Steps