Practice: Enterprise AI Solution Blueprint

# Practice: Design an AI solution for a real problem

def design_ai_solution(problem_description):
    """Framework for designing an AI solution."""
    
    print("=== AI Solution Design Framework ===")
    print()
    print(f"Problem: {problem_description}")
    print()
    
    # Example solution for a library book recommendation system
    solution = {
        "ML Type": "Collaborative Filtering + Content-Based Hybrid",
        "Input Features": [
            "User's borrowing history (book IDs and genres)",
            "Book metadata (genre, author, publication year, description)",
            "User ratings if available",
            "Time of year (seasonal preferences)",
        ],
        "Output": "Ordered list of 10 book recommendations for each user",
        "Training Data Needed": "Historical borrowing records from at least 1,000 users",
        "Evaluation Metric": "Precision@10: what fraction of recommendations does the user actually borrow?",
        "Human Oversight": "Librarians review recommendations weekly for obvious errors",
        "Privacy Considerations": [
            "Anonymize user data before training",
            "Allow users to opt out of personalized recommendations",
            "Do not share individual reading history",
        ],
        "Failure Modes": [
            "Cold start: new users with no history get generic popular book recommendations",
            "Filter bubble: users may never discover new genres",
        ],
        "Success Criteria": "10% increase in books borrowed per member per year",
    }
    
    for section, content in solution.items():
        print(f"{section}:")
        if isinstance(content, list):
            for item in content:
                print(f"  - {item}")
        else:
            print(f"  {content}")
        print()

design_ai_solution(
    "A public library wants to recommend books to members based on their interests and borrowing history"
)