Engineering judgment remains the differentiator when using AI coding tools. AI accelerates code generation but cannot replace the engineer's responsibility for edge cases, system architecture, team context, and tradeoffs. A practical workflow is outlined: use TDD to constrain AI output (write failing tests first, let AI implement, then refactor), cover critical flows with E2E tests, enforce standards via linting and git hooks, and close the feedback loop using tools like Playwright MCP and Chrome DevTools MCP. AI-generated code should be reviewed as critically as a junior developer's PR, watching for unnecessary complexity, subtle bugs, security holes, and deviations from team patterns. Comments should explain 'why' not 'how', and large AI-generated outputs should be broken into small, reviewable chunks. The core message: AI is a multiplier of existing engineering skills, not a replacement for them.