AI Engineering
Responsible, security-first AI-assisted development — leveraging AI to amplify engineering output while maintaining quality and accountability.
Workflow
Prompt
Craft precise, context-rich prompts with clear constraints. Include relevant code context, security requirements, and expected behavior.
Diff
Review every generated diff line by line. Understand what changed and why. Reject suggestions that introduce complexity without clear value.
Test
Run existing tests and write new ones for AI-generated code. AI output gets the same test coverage expectations as hand-written code.
Scan
Run SAST, DAST, and dependency scanning on all changes. AI-generated code is not exempt from security tooling.
Review
Human code review with full context. Reviewers are informed when AI assisted the change. No rubber-stamping.
Merge
Merge only after all checks pass. AI-assisted changes follow the same branch protection and approval requirements.
Responsible AI Principles
AI Augments, Never Replaces
AI tools accelerate exploration and reduce boilerplate, but every decision is human-owned. Engineers remain accountable for correctness, security, and maintainability.
Security-First AI Usage
No proprietary code in public AI models. Sensitive logic is never generated by AI without thorough review. All AI-generated code passes the same security scanning as human-written code.
Transparent Attribution
AI-assisted code is clearly marked in commits and PRs. Teams know when AI contributed to a change, enabling appropriate review rigor.
Continuous Validation
AI suggestions are validated against existing patterns, architectural decisions, and team conventions. Consistency with the codebase takes priority over AI novelty.
Copilot-Assisted Refactors
I use GitHub Copilot and Claude as refactoring accelerators — they propose changes, I validate them. Every Copilot-assisted refactor follows the same workflow: generate a candidate diff, review it against the existing architecture, run the full test suite, scan for security issues, and only then consider it for merge.
This approach has been particularly effective for large-scale rename operations, API migration boilerplate, and test generation for legacy code. In each case, the AI handles the mechanical work while the engineer focuses on correctness and edge cases.