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Engineering Reliable AI-Assisted Code

A Practical Framework for Learning, Ethical Governance, and AGI Preparation

Engineering Reliable AI-Assisted Code
This book is 100% completeLast updated on 2026-11-01
Phillip Nakata

Go beyond a plausible patch: trace its impact, choose meaningful tests, and make the merge decision reviewable.

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About the Book

The assistant wrote the patch. What makes it ready to merge? Engineering Reliable AI-Assisted Code connects a proposed change with its likely consumers, meaningful tests, review decisions, and release conditions.

Learn how to compare dependency graphs from the old and new code, keep unmapped files visible, and require broader testing when the evidence is incomplete. TypeScript and Copilot, Python, and JVM chapters explain how to supply evidence under one shared planning contract. Later chapters cover GitHub enforcement, AI code review, security, architecture, release, recovery, and adoption in shadow mode.

By Phillip R. Nakata. Approximately 10,500 words, 14 chapters, and three appendices; 29 pages in the current layout. Includes a Node.js reference planner, demonstration, fixtures, and 16 passing regression tests. Repository-specific graph collection, coverage export, test execution, and CI enforcement remain integration work. A complete first-chapter sample is available.

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Software EngineeringTestingArtificial Intelligence

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Author

About the Author

Phillip Nakata

Phillip R. Nakata develops AI agents and writes practical engineering guides with an emphasis on reproducible procedures and evidence that another operator can inspect. In Engineering Reliable AI-Assisted Code, he applies that approach to change-impact analysis, meaningful test selection and disciplined GitHub workflows. His focus is on helping developers connect an AI-assisted patch to its dependencies, verification results, review decision and recovery plan, while keeping incomplete evidence and repository-specific integration work visible.

Contents

Table of Contents

Table of Contents

How to use this book

  1. Reliability Begins Before the First Suggestion
  2. Define the Change and Preserve Its Evidence
  3. Model Dependencies Without Claiming Omniscience
  4. Compute a Conservative Impact Set
  5. Build a Credible Map from Tests to Code
  6. Select Tests Without Hiding Unknowns
  7. Apply the Method to TypeScript and Copilot
  8. Apply the Method to Python
  9. Apply the Method to JVM Applications
  10. Make GitHub Checks Enforce the Decision
  11. Review AI-Assisted Changes for Behavior
  12. Keep Security and Architecture in the Baseline
  13. Release with a Recovery Plan
  14. Adopt the Method and Measure What Matters
Practical Appendices
  • Appendix A Reference Planner and Demonstration
  • Appendix B Reusable Engineering Records
  • Appendix C Terms and Primary References

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