Chapter 32: Further Reading

Annotated Bibliography

1. Team Topologies: Organizing Business and Technology Teams for Fast Flow by Matthew Skelton and Manuel Pais (IT Revolution Press, 2019)

The foundational text on how team structure affects software delivery. While not AI-specific, the concepts of team cognitive load, interaction modes, and platform teams directly apply to organizing AI practices across teams. The "Team API" concept is particularly relevant to standardizing how teams communicate about AI-generated code. Read this to understand the organizational dynamics that shape how AI practices spread (or fail to spread) across teams.

2. Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble, and Gene Kim (IT Revolution Press, 2018)

The definitive guide to measuring software delivery performance. The DORA metrics (deployment frequency, lead time, change failure rate, time to restore service) provide a proven framework for measuring the impact of AI tools on team effectiveness. Chapter 32's discussion of velocity and quality metrics draws directly from this research. Essential for anyone designing an AI effectiveness measurement program.

3. The Pragmatic Programmer: Your Journey to Mastery (20th Anniversary Edition) by David Thomas and Andrew Hunt (Addison-Wesley, 2019)

The updated edition includes principles about code ownership, communication, and knowledge sharing that translate directly to AI-assisted development. The "Don't Repeat Yourself" principle, applied to prompts rather than code, is the intellectual foundation of shared prompt libraries. The sections on tracer bullets and prototyping inform how teams should experiment with AI practices before formalizing them.

4. An Elegant Puzzle: Systems of Engineering Management by Will Larson (Stripe Press, 2019)

Larson's framework for managing engineering organizations provides practical guidance on organizational change, team sizing, and scaling practices. His concept of "organizational debt" parallels the AI practices debt that accumulates when teams adopt AI tools without coordination. The chapters on migrations and system design are particularly relevant to scaling AI practices across organizations.

5. Working Effectively with Legacy Code by Michael Feathers (Prentice Hall, 2004)

While this book predates AI coding assistants, its principles for safely modifying unfamiliar code are directly relevant. When team members encounter AI-generated code they did not write, the techniques Feathers describes -- characterization tests, seam identification, and incremental refactoring -- are exactly what they need. Read this alongside Chapter 32's discussion of code ownership to understand how to maintain AI-generated code responsibly.

6. Thinking in Systems: A Primer by Donella Meadows (Chelsea Green Publishing, 2008)

Meadows' systems thinking framework helps explain why team AI practices behave as complex systems with feedback loops, leverage points, and emergent behaviors. Understanding concepts like reinforcing feedback (knowledge sharing begets more knowledge sharing) and balancing feedback (measurement creates accountability which maintains quality) deepens your ability to design effective team AI practices. Particularly valuable for understanding why scaling AI practices is nonlinear.

7. "The Art of Prompt Design: Prompt Boundaries and Token Healing" by Microsoft Research (2024)

This research paper explores how prompt structure affects AI output quality, with implications for team prompt standardization. The findings about prompt sensitivity -- small changes in prompt wording producing significantly different outputs -- underscore why shared prompt libraries with version control are essential for team consistency. Available through the Microsoft Research publications portal.

8. Software Engineering at Google: Lessons Learned from Programming Over Time by Titus Winters, Tom Manshreck, and Hyrum Wright (O'Reilly Media, 2020)

Google's engineering practices, particularly around code review, testing, and knowledge sharing, provide a blueprint for scaling software development practices. Chapter 32's discussion of organizational scaling, layered standards, and Centers of Excellence draws on principles articulated in this book. The chapters on large-scale changes and deprecation are especially relevant to managing prompt library evolution.

9. The Culture Map: Breaking Through the Invisible Boundaries of Global Business by Erin Meyer (PublicAffairs, 2014)

For globally distributed teams, Meyer's framework for understanding cultural differences in communication, decision-making, and trust-building is essential. AI practices that work in one cultural context may not transfer directly to another. A team that values consensus-based decision-making will adopt AI conventions differently from a team that values individual autonomy. This book helps leaders adapt AI standardization strategies to their team's cultural context.

10. Continuous Delivery: Reliable Software Releases through Build, Test, and Deploy Automation by Jez Humble and David Farley (Addison-Wesley, 2010)

The principles of continuous delivery -- automation, feedback loops, and incremental improvement -- apply directly to team AI practices. Automating prompt testing, measuring AI effectiveness continuously, and iterating on practices based on data are all continuous delivery principles applied to a new domain. The chapter on configuration management is particularly relevant to standardizing AI tool configurations.

11. "Large Language Models for Software Engineering: A Systematic Literature Review" (ACM Computing Surveys, 2025)

This comprehensive survey of academic research on LLMs in software engineering covers empirical studies of AI coding assistant effectiveness, common failure modes, and best practices for human-AI collaboration in software development. It provides the empirical foundation for many of the recommendations in Chapter 32, particularly around code review standards and quality measurement.

12. Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity by Kim Scott (St. Martin's Press, 2017)

Scott's framework for giving direct, caring feedback is essential for teams navigating AI adoption disagreements. When a team member's AI-generated code does not meet standards, or when a developer resists adopting shared practices, Radical Candor provides a communication model that is honest without being hostile. The chapter on communication patterns in Section 32.8 benefits from this perspective.

13. Building a Second Brain: A Proven Method to Organize Your Digital Life and Unlock Your Creative Potential by Tiago Forte (Atria Books, 2022)

Forte's personal knowledge management system provides a framework for organizing and sharing AI-generated knowledge within teams. His concepts of "progressive summarization" and "intermediate packets" are directly applicable to building internal AI knowledge bases and structuring prompt libraries. Teams that apply these principles to their AI knowledge sharing create more accessible and useful resources.

14. The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change by Camille Fournier (O'Reilly Media, 2017)

Fournier's guide for engineering managers at all levels includes practical advice on team building, process design, and organizational change that applies directly to leading AI adoption. The sections on building team culture, running effective meetings (relevant to show-and-tell sessions), and managing technical projects (relevant to prompt library development) provide management-level context for Chapter 32's technical recommendations.