Use AI-assisted development tools responsibly in a senior frontend workflow

Interview framing: The strongest answer is not "AI writes code for me." It is that AI accelerates work while the engineer remains accountable for design, correctness, and security.

Practical workflow:

1. Exploration:

• Ask AI to summarize unfamiliar files, trace data flow, or identify related tests.
• Verify conclusions by reading the code and running the app/tests.

2. Implementation:

• Use AI for boilerplate, repetitive component variants, migration steps, and draft tests.
• Keep changes small enough to review carefully.

3. Refactoring:

• Ask for candidate simplifications, but preserve public contracts and behavior.
• Use tests and diffs to confirm no accidental rewrites.

4. Testing:

• Generate edge-case ideas and initial test cases.
• Edit tests so they assert product behavior, not AI-generated assumptions.

5. Security and privacy:

• Do not paste secrets, customer data, or sensitive policy details into tools without approval.
• Review generated code for injection, auth bypasses, overbroad permissions, and dependency risks.

6. Team practice:

• Document non-obvious AI-assisted changes like any other change.
• Treat AI output as junior-draft code: useful, but always reviewed.

Good closing: "AI helps me move faster, but I still own the architecture, trade-offs, test coverage, and production risk."