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AI Task

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Overview

AI Tasks are designed for intelligent processing operations using artificial intelligence capabilities. These tasks can handle complex operations like text analysis, content generation, and data processing.

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Visual Example

AI Task Example

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Configuration Structure

{
    "type": "TASK",
    "block": {
        "name": "AI Task Name",
        "type": "TASK",
        "instructions": "Detailed instructions for the AI",
        "input_parameters": [],
        "expected_output": [],
        "dependencies": [],
        "error_policy": "RAISE"
    }
}

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Required Fields

FieldTypeDescriptionRequired
namestringUnique identifier for the taskYes
instructionsstringClear instructions for processingYes
input_parametersarrayInput configurationYes
expected_outputarrayOutput configurationYes
dependenciesarrayList of dependent tasksNo
error_policystringError handling strategyNo

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Input Parameters

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Text Input

{
    "name": "input_text",
    "type": "STRING",
    "description": "Text content to process",
    "required": true,
    "source": "task_config"
}

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Structured Data Input

{
    "name": "data_object",
    "type": "OBJECT",
    "description": "Structured data for analysis",
    "properties": [
        {
            "name": "field1",
            "type": "STRING",
            "description": "First field"
        },
        {
            "name": "field2",
            "type": "INTEGER",
            "description": "Second field"
        }
    ]
}

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Array Input

{
    "name": "items",
    "type": "ARRAY",
    "description": "List of items to process",
    "items": {
        "type": "STRING"
    }
}

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Output Parameters

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Basic Output

{
    "name": "result",
    "type": "STRING",
    "description": "Processing result"
}

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Analysis Output

{
    "name": "analysis",
    "type": "OBJECT",
    "properties": {
        "sentiment": {
            "type": "STRING",
            "description": "Detected sentiment"
        },
        "confidence": {
            "type": "FLOAT",
            "description": "Confidence score"
        },
        "categories": {
            "type": "ARRAY",
            "items": {
                "type": "STRING"
            },
            "description": "Detected categories"
        }
    }
}

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Common Use Cases

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1. Text Analysis

{
    "name": "Analyze Text",
    "instructions": "Analyze the input text for sentiment and key topics",
    "input_parameters": [
        {
            "name": "text",
            "type": "STRING",
            "description": "Text to analyze",
            "required": true
        }
    ],
    "expected_output": [
        {
            "name": "analysis",
            "type": "OBJECT",
            "properties": {
                "sentiment": "STRING",
                "topics": "ARRAY",
                "summary": "STRING"
            }
        }
    ]
}

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2. Content Generation

{
    "name": "Generate Content",
    "instructions": "Generate content based on provided parameters",
    "input_parameters": [
        {
            "name": "topic",
            "type": "STRING",
            "description": "Content topic",
            "required": true
        },
        {
            "name": "style",
            "type": "STRING",
            "description": "Writing style",
            "required": true
        },
        {
            "name": "length",
            "type": "INTEGER",
            "description": "Target word count",
            "required": true
        }
    ],
    "expected_output": [
        {
            "name": "content",
            "type": "STRING",
            "description": "Generated content"
        }
    ]
}

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Best Practices

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1. Writing Instructions

✅ Do:
  • Be specific and clear
  • Include examples when helpful
  • Specify format requirements
  • Define expected behavior
❌ Don’t:
  • Use vague descriptions
  • Omit important details
  • Assume context

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2. Input Configuration

✅ Do:
  • Validate input types
  • Set required fields
  • Provide clear descriptions
  • Use appropriate types
❌ Don’t:
  • Use overly complex structures
  • Skip parameter descriptions
  • Ignore validation

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3. Output Configuration

✅ Do:
  • Define clear structure
  • Include all necessary fields
  • Document format requirements
  • Handle error cases
❌ Don’t:
  • Use ambiguous types
  • Omit error handling
  • Ignore edge cases

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Error Handling

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Error Policies

{
    "error_policy": "RAISE", // Stop on error
    "error_policy": "IGNORE", // Continue execution
    "error_policy": "RETRY" // Retry on failure
}

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Validation

  1. Input validation
  2. Output validation
  3. Error reporting

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Examples

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Text Classification

{
    "name": "Classify Text",
    "instructions": "Classify the input text into predefined categories",
    "input_parameters": [
        {
            "name": "text",
            "type": "STRING",
            "description": "Text to classify",
            "required": true
        },
        {
            "name": "categories",
            "type": "ARRAY",
            "items": {
                "type": "STRING"
            },
            "description": "Available categories",
            "required": true
        }
    ],
    "expected_output": [
        {
            "name": "classification",
            "type": "OBJECT",
            "properties": {
                "category": "STRING",
                "confidence": "FLOAT",
                "alternatives": "ARRAY"
            }
        }
    ]
}

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Common Issues and Solutions

IssueSolution
Unclear InstructionsProvide specific, detailed instructions
Input Validation FailsCheck input types and requirements
Output MismatchVerify output structure matches requirements
Performance IssuesOptimize input/output size

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Integration with Other Tasks

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Passing Results