AI-ML Documentation in Action: Real-World Practice Projects

“In theory, there is no difference between theory and practice. In practice, there is.” — Yogi Berra

Remember when you first tried to ride a bike? Reading a manual about balancing and pedaling made perfect sense—until you actually sat on the seat and realized that knowing and doing live in completely different universes.

Documenting AI systems works exactly the same way. You’ve absorbed all this knowledge about neural architectures, training paradigms, and audience adaptation. But now comes the fun part: putting those skills to work on challenges that mirror what you’ll face in the wild.

Let’s turn theory into muscle memory.

Why Practice Projects Matter: The Documentation Dojo

Imagine two documentation specialists:

Alex has read every book on AI documentation, follows all the thought leaders on Twitter, and can quote best practices in his sleep. When asked to document a real system, he freezes like a deer in headlights, unsure where to begin.

Jamie has complemented her theoretical knowledge with dozens of practice runs. She’s made mistakes in safe environments, received feedback, and refined her approach. When faced with a new system to document, she has a mental playbook ready.

These projects are designed to turn you into Jamie.

Project 1: The Jargon Translator Challenge

The Scenario

You’re working at “FutureSight,” a computer vision startup that has developed a cutting-edge object detection model for autonomous vehicles. The engineering team has written a technical whitepaper about their model architecture that’s incomprehensible to everyone except other Ph.D.s in computer vision.

The CEO needs you to transform this technical masterpiece into something the sales team can understand and communicate to potential customers—without sacrificing accuracy.

Your Mission

1. Take this deliberately jargon-heavy paragraph and rewrite it for a non-technical audience:

Our proprietary detection framework implements a multi-scale feature pyramid network with deformable convolutions and focal loss optimization. The backbone utilizes an EfficientNet-B4 architecture pretrained on ImageNet, fine-tuned using mixed precision training with the AdamW optimizer. We've achieved state-of-the-art mean Average Precision (mAP) of 0.87 on the internal benchmark dataset, with inference latency of 17ms on our edge hardware, making it suitable for real-time detection tasks in constrained computational environments.

1. Create a visual analogy that explains how the model works to non-technical stakeholders.
2. Develop a “translation glossary” with 5-7 key technical terms that sales team members are likely to encounter in customer conversations.

Success Looks Like

• Clear explanation that’s accessible to non-engineers
• No false simplifications (don’t say “AI magic”)
• Retention of the meaningful performance characteristics
• Visual elements that reinforce understanding

Project 2: The API Documentation Challenge

The Scenario

“SentimentSage” is launching a new sentiment analysis API that allows developers to submit text and receive detailed sentiment scores. You’ve been tasked with creating the documentation for the primary endpoint.

The engineers have given you this JSON sample response and nothing else:

{
  "text": "The new feature is impressive but the app crashes frequently.",
  "overall_sentiment": 0.2,
  "sentiment_breakdown": {
    "positive": 0.6,
    "negative": 0.4,
    "neutral": 0.0
  },
  "entity_sentiments": [
    {
      "entity": "new feature",
      "sentiment": 0.8,
      "confidence": 0.92
    },
    {
      "entity": "app",
      "sentiment": -0.7,
      "confidence": 0.85
    }
  ],
  "emotional_tones": {
    "impressed": 0.65,
    "frustrated": 0.55,
    "satisfied": 0.2
  },
  "language_detected": "en",
  "processing_time_ms": 78
}

Your Mission

1. Create comprehensive documentation for this API endpoint, including:
   • Endpoint description and purpose
   • Request parameters
   • Response field descriptions
   • Example requests and responses
   • Error handling
   • Usage limits and performance considerations

2. Add a “Getting Started” section that shows developers how to use this endpoint in a real application with code examples in at least two programming languages.

3. Include clear explanations of how to interpret the sentiment scores and confidence values.

Success Looks Like

• Developer-friendly documentation that explains both the “how” and “why”
• Clear explanations of each field in the response
• Practical code examples that developers can copy/paste
• Proper API documentation formatting (consider using standardized formats like OpenAPI)
• Guidance on handling edge cases and errors

Project 3: The Model Card Challenge

The Scenario

“TextCraft” has just fine-tuned a large language model for code generation. The model will be released open-source, and you need to create a comprehensive model card that documents everything users need to know—from capabilities to limitations and ethical considerations.

You know these basic details:

• The model was fine-tuned from CodeLlama-13B
• Training data included GitHub repositories with MIT, Apache, and BSD licenses
• It excels at Python and JavaScript but struggles with less common languages
• It sometimes generates non-functioning code when prompted for complex algorithms
• The model was evaluated on HumanEval and MBPP benchmarks
• It shows some bias toward certain coding styles and occasionally generates insecure code

Your Mission

Create a complete model card following the framework from Model Cards for Model Reporting (Mitchell et al.), with these sections:

1. Model Details
   • Basic model information (architecture, training, etc.)
   • Intended uses and domains
2. Performance Evaluation
   • Benchmark results
   • Performance across different programming languages
   • Testing methodologies
3. Limitations and Biases
   • Known shortcomings
   • Potential biases in code generation
   • Security considerations
4. Ethical Considerations
   • Potential misuse cases
   • Environmental impact of model training
   • Intellectual property considerations
5. Usage Guidelines
   • Recommended prompting strategies
   • Best practices for verification
   • Deployment considerations

Success Looks Like

• Comprehensive documentation that covers technical and ethical aspects
• Clear articulation of model capabilities and limitations
• Practical guidance for users of the model
• Transparent discussion of potential issues
• Professional presentation similar to model cards from leading organizations

Project 4: The Interactive Tutorial Challenge

The Scenario

“ImageInsight” has created an image analysis platform for medical professionals. The tool uses AI to help identify anomalies in medical imaging, but many physicians are hesitant to adopt it due to lack of understanding about how it works and where it can fit into their workflow.

You’ve been asked to create an interactive tutorial that builds both understanding and confidence in the system.

Your Mission

Design a notebook-style tutorial (conceptually similar to Jupyter Notebooks) that:

1. Introduces the platform’s capabilities with real-world medical examples
2. Explains the underlying AI technologies at a level appropriate for medical professionals
3. Provides interactive elements where users can:
   • Upload sample images (you can simulate this in your documentation)
   • Adjust key parameters and see the results
   • Follow a guided workflow for common use cases
4. Addresses common concerns and questions about AI reliability in medical contexts
5. Includes checkpoints that confirm understanding before moving to more advanced topics

Success Looks Like

• An engaging, step-by-step tutorial that builds confidence
• Explanations tailored specifically for medical professionals (not too technical, not too simplified)
• Interactive elements clearly described
• Anticipation of common questions and concerns
• A structure that gradually builds understanding

Project 5: The Multimodal Explanation Challenge

The Scenario

“VoiceVision” has developed a multimodal AI system that takes both voice input and camera images to control smart home devices. The system is powerful but complex, and users are struggling to understand its capabilities and limitations.

You need to create a comprehensive explanation of this multimodal system that helps users understand:

• How the voice and vision components work together
• When to use which modality
• How the system handles conflicts between voice and visual inputs
• Common failure modes and how to avoid them

Your Mission

Create a user-friendly explanation of the multimodal system that includes:

1. A conceptual overview of how voice and vision technologies work together
2. A decision tree that helps users understand when to use each modality
3. Visualizations of the typical processing flow from input to action
4. A troubleshooting guide that addresses common failure scenarios
5. “Mental models” that help users conceptualize how the system works

The twist: Your explanation must work across three formats:

• A web-based documentation page
• A printable quick-start guide
• Voice-assistive documentation (describe how this would work)

Success Looks Like

• Clear explanation of complex multimodal technology
• Effective visualizations that enhance understanding
• Adaptable content that works across multiple formats
• Practical guidance that solves real user problems
• Documentation that builds accurate mental models

The Documentation Critique Project (Bonus Challenge)

The Scenario

You’ve been hired as a documentation consultant to evaluate and improve existing AI documentation. You’ve been given examples from three companies and asked to provide a detailed critique and improvement plan.

Your Mission

Select a real AI documentation example (from OpenAI, Google Cloud AI, Hugging Face, or another major AI provider) and:

1. Perform a thorough analysis identifying:
   • Strengths that make the documentation effective
   • Weaknesses that create friction for users
   • Missing elements that would enhance understanding
   • Organizational improvements that would help navigation
2. Create a detailed improvement plan with:
   • High-priority changes with specific examples
   • Suggested reorganization if needed
   • Additional content recommendations
   • Visual enhancements that would improve comprehension
3. Rewrite one section as an example of your recommended improvements

Success Looks Like

• Thoughtful, specific critique that demonstrates documentation expertise
• Practical recommendations that could realistically be implemented
• Clear differentiation between high and low priority improvements
• An example rewrite that showcases significant improvement

Bringing It All Together: Your Documentation Portfolio

Each of these projects exercises different documentation muscles:

• Explaining complex technology to non-technical audiences
• Creating developer-focused technical documentation
• Documenting model capabilities and limitations
• Building interactive learning experiences
• Explaining multimodal systems with appropriate visuals
• Evaluating and improving existing documentation

By completing these projects, you’re not just practicing—you’re building a portfolio that demonstrates your ability to handle the full spectrum of AI documentation challenges.

Next Steps: Getting Feedback on Your Work

Documentation rarely exists in a vacuum—getting feedback is essential to improvement. Here are some ways to get input on your practice projects:

1. Peer review: Share with other documentation specialists or technical writers
2. Technical accuracy: Ask engineers or data scientists to review for correctness
3. User testing: Find someone from your target audience and see if they understand
4. Documentation communities: Share in forums like Write the Docs for professional feedback

Remember, great documentation is never “done”—it evolves as systems change, users provide feedback, and you gain new insights.

Now, roll up your sleeves and start documenting. Your AI-ML documentation skills are about to level up from “theoretical knowledge” to “battle-tested expertise.”