Introduction — AI Is a Tool, Discipline Is the Difference
Chapter 1 — What AI Is Good At
Chapter 2 — What AI Is Bad At
Chapter 3 — When Not to Use AI
Chapter 4 — Start With the Problem, Not the Model
Chapter 5 — Decomposing Work
Chapter 6 — Context Engineering
Chapter 7 — Prompting as Specification
Chapter 8 — Choosing Models and Tools
Chapter 9 — Retrieval, Tools, and External Knowledge
Chapter 10 — Generation Without AI Slop
Chapter 11 — AI-Assisted Software Development
Chapter 12 — AI-Assisted Research and Analysis
Chapter 13 — AI-Assisted Writing and Documentation
Chapter 14 — AI-Assisted Data and Analytical Work
Chapter 15 — Multimodal AI
Chapter 16 — Never Confuse Generation With Completion
Chapter 17 — Human Editing and Review
Chapter 18 — Testing AI-Generated Software
Chapter 19 — Evaluating AI Systems
Chapter 20 — Critique, Verification, and Independent Checks
Chapter 21 — Provenance and Traceability
Chapter 22 — Architecture for AI Applications
Chapter 23 — AI Pipelines
Chapter 24 — Agents and Autonomous Workflows
- Agent-to-agent communication
Chapter 25 — State, Memory, and Knowledge
Chapter 26 — Structured Outputs and Interfaces
- Deterministic boundaries and type systems
Chapter 27 — From Prototype to Production
Chapter 28 — Observability
Chapter 29 — Cost and Performance Engineering
Chapter 30 — Reproducibility and Change Management
Chapter 31 — Continuous Improvement
Chapter 32 — AI Security Fundamentals
Chapter 33 — Secrets, Privacy, and Confidential Information
- Confidential information throughout the AI pipeline
Chapter 34 — Intellectual Property and Licensing
Chapter 35 — Bias, Fairness, and Harm
Chapter 36 — Designing for Failure
Chapter 37 — Human-in-the-Loop Development
Chapter 38 — Avoiding Automation Bias
Chapter 39 — AI and the Development Team
Chapter 40 — AI Policies That People Can Actually Follow
Chapter 41 — Training People to Work With AI
Chapter 42 — Measuring Whether AI Is Actually Helping
Chapter 43 — The Disciplined AI Development Loop
Chapter 44 — Designing Review Gates
Chapter 45 — Building an AI Development Playbook
Chapter 46 — AI Development Maturity
Chapter 47 — The Future of Disciplined AI Development
Appendices
