Documenting AI APIs

Let’s be honest: API documentation is rarely anyone’s favorite task. But when it comes to AI services, great documentation isn’t just helpful—it’s essential. AI APIs introduce unique challenges that traditional documentation approaches don’t fully address.

Think about it: How do you explain confidence scores to someone who’s never worked with probabilistic outputs? How do you document the subtle differences between similar AI models? How do you help developers understand why their query works perfectly one day but fails mysteriously the next?

Did you know? A 2022 survey found that 78% of developers cite poor or incomplete documentation as the number one reason they abandon an API. For AI APIs specifically, this number jumps to 86%.

In this module, we’ll explore how to create documentation that not only explains how your AI API works but helps developers truly understand, trust, and effectively use it. Whether you’re documenting a computer vision API, a language model, or any other AI service, you’ll learn practical strategies to make your documentation shine.

Want to master API documentation? Check out our Complete API Documentation Course for comprehensive training on creating professional-grade API documentation that developers love.

Unique aspects of AI API documentation

What Makes AI APIs Different?

Traditional API documentation focuses primarily on endpoints, parameters, and return values. But AI APIs require additional considerations:

Non-Deterministic Behavior

Unlike traditional APIs where the same input reliably produces the same output, AI APIs may return different results for the same input due to:

  • Model updates and improvements
  • Randomness in model inference
  • Contextual factors affecting prediction
# Example: The same text may receive different sentiment scores
response1 = sentiment_api.analyze("This product is great")  # 0.92 positive
# Later request with the exact same text:
response2 = sentiment_api.analyze("This product is great")  # 0.89 positive

Confidence Scores and Thresholds

AI APIs often return confidence scores that developers need to interpret:

{
  "sentiment": "positive",
  "confidence": 0.92,
  "entities": [
    {"entity": "product", "confidence": 0.97},
    {"entity": "feature", "confidence": 0.64}
  ]
}

Without proper documentation, developers might not know what a “good” confidence score is for your specific model.

Edge Cases and Limitations

All AI models have limitations and edge cases that need explicit documentation:

  • Input types that may produce unreliable results
  • Known biases in training data that affect predictions
  • Performance degradation with certain inputs (language, image quality, etc.)

Ethical and Safety Guidelines

AI APIs often require ethical usage guidelines:

  • Appropriate and inappropriate use cases
  • Privacy implications when processing user data
  • Content policies for generative AI

Documentation Types Matrix

Types of AI API Documentation

Great AI API documentation is layered to serve different audiences and needs:

Getting Started Guides

Getting started tutorials help developers quickly understand what your API does and how to implement basic functionality.

A good getting started guide should:

  1. Clearly state prerequisites
  2. Provide simple setup instructions
  3. Include working code examples
  4. Show expected output
  5. Indicate time to completion

Anatomy of an Effective Tutorial

For AI APIs specifically, getting started guides should also:

  • Explain how to interpret confidence scores
  • Show both successful and low-confidence responses
  • Include examples across different types of inputs

API Reference

The API reference is your comprehensive, endpoint-by-endpoint documentation. For AI APIs, each endpoint should document:

  • Required and optional parameters
  • Example requests and responses
  • Model versions and their differences
  • Input constraints (size, format, content)
  • Expected latency and throttling limits
## POST /v1/vision/detect

Detects objects in the provided image with confidence scores.

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| image | file/URL | Yes | Image to analyze. Max size: 5MB. Formats: JPG, PNG |
| min_confidence | float | No | Minimum confidence threshold (0.0-1.0). Default: 0.5 |
| max_results | integer | No | Maximum objects to return. Default: 10 |

### Model Versions

| Version | Default | Features | Notes |
|---------|---------|----------|-------|
| v2.1 | Yes | General objects, faces, text | Optimized for real-time |
| v1.8 | No | General objects only | Legacy, available until 2024 |

### Example Request (Python)

```python
import requests

response = requests.post(
    'https://api.example.com/v1/vision/detect',
    files={'image': open('dog.jpg', 'rb')},
    data={'min_confidence': 0.6}
)

Example Response

{
  "objects": [
    {
      "label": "dog",
      "confidence": 0.98,
      "bounding_box": {
        "x": 125,
        "y": 80,
        "width": 200,
        "height": 300
      }
    },
    {
      "label": "person",
      "confidence": 0.87,
      "bounding_box": {...}
    }
  ],
  "model_version": "v2.1",
  "process_time_ms": 328
}

Notes

  • Lower lighting conditions may reduce detection accuracy
  • For real-time applications, consider using min_confidence of 0.7 or higher ```

Concepts and Explanations

Concept guides help developers understand the AI-specific aspects of your service. These should include:

  • Explanations of how your models work (at a high level)
  • Interpretation of confidence scores and thresholds
  • Model versioning policy and compatibility
  • Performance characteristics and optimization tips

Error Documentation

AI APIs introduce unique error scenarios that should be carefully documented:

  • Input validation failures specific to AI (e.g., “image too low resolution”)
  • Model-specific errors (e.g., “no faces detected in image”)
  • Confidence threshold failures
  • Safety filter rejections

Each error should include:

  • Error code and description
  • Possible causes
  • Recommended solutions
  • Example error responses

Error Documentation Template

Documenting Confidence Scores

Confidence scores often confuse developers new to AI. Your documentation should include:

  1. What confidence means in the context of your specific models
  2. Recommended thresholds for different use cases
  3. Visual examples of results at different confidence levels
  4. Code examples for filtering by confidence

Confidence Score Visualization

Example Documentation for Confidence Scores

## Understanding Confidence Scores

Our sentiment analysis API returns confidence scores between 0.0 and 1.0 that indicate the model's certainty in its prediction.

### Recommended Thresholds

| Threshold | Use Case |
|-----------|----------|
| 0.9+ | Critical applications requiring high accuracy |
| 0.7-0.9 | Standard business applications |
| 0.5-0.7 | Exploratory analysis where recall is important |
| <0.5 | Not recommended for decision-making |

### Handling Low-Confidence Results

We recommend implementing a fallback strategy for low-confidence results:

```python
response = sentiment_api.analyze("This text is ambiguous")

if response.confidence < 0.7:
    # Option 1: Flag for human review
    queue_for_review(response)
    
    # Option 2: Take a neutral action
    take_neutral_path()
    
    # Option 3: Request additional input
    ask_for_clarification()


## Documenting Input Validation

All AI models have specific input requirements. Your documentation should clearly explain:

1. Format requirements (file types, sizes, encoding)
2. Content requirements (quality, clarity, completeness)
3. Preprocessing recommendations
4. Common validation errors and solutions

![Input Validation Diagram](/aimldocumentation/assets/images/api-documentation/input-validation-diagram.svg)

### Example Input Validation Documentation

For an image recognition API:

```markdown
## Input Requirements

### Format
- Supported formats: JPG, PNG, WebP
- Maximum file size: 10MB
- Minimum dimensions: 224x224 pixels
- Maximum dimensions: 4096x4096 pixels

### Content Quality
- Images should be well-lit and in focus
- Subject should be clearly visible and not obscured
- For best results, the subject should occupy at least 30% of the image

### Preprocessing Recommendations
- For profile detection, crop images to 1:1 aspect ratio
- For document scanning, use high contrast settings
- For multi-object detection, ensure adequate spacing between objects

### Common Validation Errors

| Error Code | Description | Solution |
|------------|-------------|----------|
| ERR_FORMAT_INVALID | Unsupported file format | Convert to JPG or PNG |
| ERR_IMAGE_TOO_SMALL | Image dimensions below minimum | Provide larger image or upscale |
| ERR_LOW_QUALITY | Image too blurry or dark | Improve lighting or clarity |

Documenting API Versions

AI models evolve rapidly, necessitating clear version documentation:

  1. Version timeline showing current and deprecated versions
  2. Differences between versions (capabilities, performance, breaking changes)
  3. Migration guides for upgrading
  4. End-of-life dates for deprecated versions

Versioning Documentation

Example Version Documentation

## API and Model Versions

Our API versions (v1, v2, etc.) are separate from our model versions (1.0, 2.3, etc.).

### API Versions

| API Version | Status | End of Support | Key Changes |
|-------------|--------|----------------|------------|
| v3 | Current | - | Added batch processing, improved error handling |
| v2 | Maintained | December 2024 | Added entity extraction, improved sentiment accuracy |
| v1 | Deprecated | June 2023 | Original release, basic sentiment only |

### Model Versions

| Model | API Compatibility | Features | Performance |
|-------|-------------------|----------|------------|
| SentimentNet 3.1 | v2, v3 | Sentiment + entities + key phrases | 94% accuracy |
| SentimentNet 2.5 | v2, v3 | Sentiment + entities | 91% accuracy |
| SentimentNet 1.0 | v1 only | Basic sentiment | 83% accuracy |

### Migration Guide: v2 to v3

```diff
- import sentimentapi.v2 as api
+ import sentimentapi.v3 as api

  client = api.Client(api_key)
  
- result = client.analyze_text(text)
+ result = client.analyze(text)  // Method name simplified

  // New response format includes confidence per entity
- print(result.entities)  // ["product", "feature"]
+ print(result.entities)  // [{"entity": "product", "confidence": 0.97}, ...]


## Creating Interactive Documentation

Static documentation can only take users so far. Interactive elements help developers understand and test AI APIs more effectively:

1. **Live API testing consoles** with configurable parameters
2. **Visual result explorers** for image/video/audio APIs
3. **Input/output playgrounds** for text-based APIs
4. **Confidence threshold sliders** to visualize impacts

![Interactive API Documentation Example](/aimldocumentation/assets/images/api-documentation/interactive-api-docs.svg)

### Implementation Tips for Interactive Documentation

Interactive documentation requires more development effort but greatly increases adoption:

1. **Start small** with a basic API console for testing endpoints
2. **Add visualization components** for AI-specific outputs
3. **Include confidence adjustments** to show threshold effects
4. **Provide one-click code generation** in multiple languages

```javascript
// Sample code for a simple API console component
const ApiConsole = () => {
  const [inputText, setInputText] = useState('');
  const [confidence, setConfidence] = useState(0.7);
  const [results, setResults] = useState(null);
  
  const handleSubmit = async () => {
    const response = await fetch('/api/sentiment', {
      method: 'POST',
      body: JSON.stringify({ 
        text: inputText,
        min_confidence: confidence
      })
    });
    
    const data = await response.json();
    setResults(data);
  };
  
  return (
    <div className="api-console">
      <textarea 
        value={inputText} 
        onChange={(e) => setInputText(e.target.value)}
        placeholder="Enter text to analyze..."
      />
      
      <div className="confidence-slider">
        <label>Confidence Threshold: {confidence}</label>
        <input 
          type="range" 
          min="0" 
          max="1" 
          step="0.1"
          value={confidence}
          onChange={(e) => setConfidence(parseFloat(e.target.value))} 
        />
      </div>
      
      <button onClick={handleSubmit}>Analyze</button>
      
      {results && (
        <div className="results">
          <h3>Results:</h3>
          <pre>{JSON.stringify(results, null, 2)}</pre>
        </div>
      )}
    </div>
  );
};

Best Practices Checklist

Use this checklist to ensure your AI API documentation covers all the essentials:

  • Clear getting started guides with working examples
  • Comprehensive API reference with all endpoints and parameters
  • Explanation of confidence scores and recommended thresholds
  • Detailed input requirements and validation rules
  • Model versioning and compatibility information
  • Common error scenarios and troubleshooting guides
  • Interactive elements for testing and visualization
  • Ethical usage guidelines and limitations
  • Performance characteristics and optimization tips
  • Updated example code in multiple languages

Exercise: Evaluating AI API Documentation

Let’s put your knowledge to the test! Examine the documentation for one of these popular AI APIs:

  1. OpenAI API
  2. Google Cloud Vision API
  3. AWS Rekognition

Answer these questions:

  • How do they document confidence scores and thresholds?
  • How do they handle model versioning?
  • What interactive elements do they include?
  • What could they improve in their documentation?

Key Takeaways

  • AI API documentation needs to go beyond traditional API docs to address non-deterministic behavior, confidence scores, and model limitations
  • Layered documentation serves different audiences and use cases
  • Great documentation includes both reference material and conceptual explanations
  • Interactive elements significantly improve developer understanding and adoption
  • Clear documentation of versioning, error handling, and confidence interpretation is essential for AI APIs

Remember: The goal isn’t just to document how your API works, but to help developers build trust in your AI service and use it effectively in their applications.

Additional Resources

Previous: AI and ML Fundamentals Next: Documenting AI Models
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