فهرست مطالب

• The Art of AI Product Development
• Brief Contents
• Contents
• Preface
• Acknowledgments
• About this Book
• About the Author
• About the Cover Illustration
• Part 1: Discovery
• Chapter 1: Creating value with AI-driven products
• 1.1 Building with AI: The reality check
• 1.2 A retrospective
• 1.3 The anatomy of an AI system
• 1.4 Learning with this book
• Chapter 2: Discovering and prioritizing AI opportunities
• 2.1 Sourcing AI ideas and opportunities
• 2.2 Prioritizing AI opportunities
• 2.3 Shaping your opportunities
• Chapter 3: Mapping the AI solution space
• 3.1 Data
• 3.2 Different types of intelligence
• 3.3 User experience
• Part 2: Development
• Chapter 4: Predictive AI
• 4.1 Unsupervised learning
• 4.2 Supervised learning
• 4.3 Time series and trend analysis
• 4.4 Personalized recommendations
• Chapter 5: Exploring and evaluating language models
• 5.1 How language models work
• 5.2 Usage scenarios for language models
• 5.3 Mapping the language model landscape
• 5.4 Managing the language model lifecycle
• Chapter 6: Prompt engineering
• 6.1 Basics of prompt engineering
• 6.2 Few-shot prompting: Learning by analogy
• 6.3 Injecting reasoning into language models
• 6.4 Best practices for prompt engineering
• Chapter 7: Search and retrieval-augmented generation
• 7.1 Specializing your language model with custom data
• 7.2 Retrieving relevant documents with semantic search
• 7.3 Building an end-to-end RAG system
• Chapter 8: Fine-tuning language models
• 8.1 Uncovering opportunities for fine-tuning
• 8.2 Fine-tuning language models for different objectives
• Chapter 9: Automating workflows with agentic AI
• 9.1 Providing language models with access to external tools
• 9.2 Assembling the agent system
• 9.3 Building at the frontier of AI agents
• 9.4 Trends and opportunities for AI agents
• Part 3: Adoption
• Chapter 10: AI user experience: Designing for uncertainty
• 10.1 Discovery and user research
• 10.2 Designing the UI
• 10.3 Collecting feedback and co-creating with your users
• Chapter 11: AI governance
• 11.1 Security: Protecting sensitive assets
• 11.2 Privacy: Maintaining trust through transparency
• 11.3 Mitigating bias in AI systems
• 11.4 Providing transparency
• 11.5 A proactive approach to AI governance
• Chapter 12: Working with your stakeholders
• 12.1 Efficient cross-functional collaboration in the AI team
• 12.2 Getting buy-in from business stakeholders
• 12.3 Communicating with customers and users
• Appendix: AI development toolbox
• Index