Patterns of Application Development Using AI

Unlock the power of AI in your applications with this groundbreaking book on AI-driven application architecture. Discover practical patterns and principles for building intelligent, adaptive, and user-centric software systems that harness the potential of large language models and AI components.

Obie Fernandez

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About the Book

"Patterns of Application Development Using AI" is a groundbreaking book that explores the intersection of artificial intelligence (AI) and application development. In this book, Obie Fernandez, a renowned software developer and co-founder of AI-powered consultant platform Olympia, shares his invaluable insights and experiences from a year-long journey of building an AI-powered application.

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Through a compelling combination of narrative chapters and practical pattern references, Obie presents a comprehensive guide to leveraging the power of large language models (LLMs) in application development. He introduces innovative patterns such as the "Multitude of Workers," "Self-Healing Data," and "Contextual Content Generation," which empower developers to build intelligent, adaptive, and user-centric applications.

Unlike other books on AI that focus on theoretical concepts or delve into the intricacies of machine learning algorithms, this book takes a pragmatic approach. It provides concrete examples, real-world use cases, and actionable advice on how to integrate AI components and functions into application architectures. Obie shares his successes, challenges, and lessons learned, offering a unique perspective on the practical application of AI in software development.

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About the Author

Obie Fernandez

Obie Fernandez began his career as a professional programmer in the early ’90s. From Java to Ruby, Lean Startup, Serverless, and now AI, Obie’s expertise and forward-thinking attitude has consistently placed him on the bleeding edge of innovation. As the author of "The Rails Way," his contributions to the Ruby on Rails community are widely recognized and respected. Obie’s role as CTO on the founding team of Andela, along with successful entrepreneurial exits, underscores his leadership and business acumen. His latest venture, Olympia, provides AI-powered virtual staffing startup. Obie’s creativity extends into the world of electronic music production and DJ’ing. He has found success in the techno, trance, and progressive house genres, playing at large EDM festivals, and releasing music on reputable labels since 2018.

Episode 24

An Interview with Obie Fernandez

Table of Contents

- Foreword by Gregor Hohpe
- Preface
  - About the Book
  - About the Code Examples
  - What I Don’t Cover
  - Who This Book Is For
  - Building a Common Vocabulary
  - Getting Involved
  - Acknowledgments
  - What’s with the illustrations?
  - About Lean Publishing
  - About The Author
- Introduction
  - Thoughts on Software Architecture
  - What is a Large Language Model?
  - Understanding Inference
  - Thinking About Performance
  - Experimenting With Different LLM Models
  - Compound AI Systems
Part 1: Fundamental Approaches & Techniques
- Narrow The Path
  - Latent Space: Incomprehensibly Vast
  - How The Path Gets “Narrowed”
  - Raw Versus Instruct-Tuned Models
  - Prompt Engineering
  - Prompt Distillation
  - What about fine-tuning?
- Retrieval Augmented Generation (RAG)
  - What is Retrieval Augmented Generation?
  - How Does RAG Work?
  - Why Use RAG in Your Applications?
  - Implementing RAG in Your Application
  - Proposition Chunking
  - Real-World Examples of RAG
  - Intelligent Query Optimization (IQO)
  - Reranking
  - RAG Assessment (RAGAs)
  - Challenges and Future Outlook
- Multitude of Workers
  - AI Workers As Independent Reusable Components
  - Account Management
  - E-commerce Applications
  - Healthcare Applications
  - AI Worker as a Process Manager
  - Integrating AI Workers Into Your Application Architecture
  - Composability and Orchestration of AI Workers
  - Combining Traditional NLP with LLMs
- Tool Use
  - What is Tool Use?
  - The Potential of Tool Use
  - The Tool Use Workflow
  - Best Practices for Tool Use
  - Composing and Chaining Tools
  - Future Directions
- Stream Processing
  - Implementating a ReplyStream
  - The “Conversation Loop”
  - Auto Continuation
  - Conclusion
- Self Healing Data
  - Practical Case Study: Fixing Broken JSON
  - Considerations and Counterindications
- Contextual Content Generation
  - Personalization
  - Productivity
  - Rapid Iteration and Experimentation
  - AI Powered Localization
  - The Importance of User Testing and Feedback
- Generative UI
  - Generating Copy for User Interfaces
  - Defining Generative UI
  - Example
  - The Shift to Outcome-Oriented Design
  - Challenges and Considerations
  - Future Outlook and Opportunities
- Intelligent Workflow Orchestration
  - Business Need
  - Key Benefits
  - Key Patterns
  - Exception Handling and Recovery
  - Implementing Intelligent Workflow Orchestration in Practice
  - Monitoring and Logging
  - Scalability and Performance Considerations
  - Testing and Validation of Workflows
Part 2: The Patterns
- Prompt Engineering
  - Chain of Thought
  - Mode Switch
  - Role Assignment
  - Prompt Object
  - Prompt Template
  - Structured IO
  - Prompt Chaining
  - Prompt Rewriter
  - Response Fencing
  - Query Analyzer
  - Query Rewriter
  - Ventriloquist
- Discrete Components
  - Predicate
  - API Facade
  - Result Interpreter
  - Virtual Machine
  - Specification and Testing
- Human In The Loop (HITL)
  - High-Level Patterns
  - Escalation
  - Feedback Loop
  - Passive Information Radiation
  - Collaborative Decision Making (CDM)
  - Continuous Learning
  - Ethical Considerations
  - Technological Advancements and Future Outlook
- Intelligent Error Handling
  - Traditional Error Handling Approaches
  - Contextual Error Diagnosis
  - Intelligent Error Reporting
  - Predictive Error Prevention
  - Smart Error Recovery
  - Personalized Error Communication
  - Adaptive Error Handling Workflow
- Quality Control
  - Eval
  - Guardrail
  - Guardrails and Evals: Two Sides of the Same Coin
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