How do you use AI for coding?
Company: Expedia
Role: Software Engineer
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Technical Screen
An interviewer asks about your day-to-day use of AI coding assistants.
Answer the following:
1) Do you use AI to write code at work? If yes, for what tasks and how often?
2) Describe your typical workflow (e.g., prompting, validating outputs, integrating into PRs, testing).
3) What benefits have you seen (productivity, quality, learning, onboarding, etc.)?
4) What risks or downsides do you watch for (hallucinations, security/privacy, IP, bias, maintainability, over-reliance)?
5) How do you mitigate those risks in practice?
6) If you could improve AI coding tools, what would you change (product, process, guardrails, evaluation)?
Quick Answer: This question evaluates a candidate's practical experience and judgment in using AI coding assistants, covering workflow integration, productivity and quality impacts, risk awareness (security, privacy, IP, hallucinations), and mitigation strategies within software engineering.
Solution
### What a strong answer should cover
Organize your response into: **(a) where you use it, (b) how you use it safely, (c) impact, (d) limits and mitigations, (e) improvements**. Interviewers are typically probing judgment, engineering rigor, and security awareness—not whether you “like AI.”
### 1) Where and when you use AI
Give concrete, non-sensitive examples:
- **Idea generation / design scaffolding:** exploring approaches, tradeoffs, edge cases.
- **Boilerplate / repetitive code:** DTOs, mappers, simple CRUD, config snippets.
- **Refactors:** renaming, extracting functions, translating patterns.
- **Testing:** generating unit test skeletons and edge-case lists.
- **Documentation:** README drafts, API usage examples.
Also mention where you *don’t* use it:
- Security-critical logic, authZ/authN flows, payment/PII handling, or any area requiring strict correctness unless you can thoroughly verify.
### 2) A practical workflow (shows engineering maturity)
A good workflow sounds like this:
1. **State the goal + constraints** (language, performance, style, dependencies, interfaces).
2. **Ask for reasoning artifacts**: assumptions, edge cases, complexity, failure modes.
3. **Generate a small, testable slice** rather than a full system in one shot.
4. **Verify like you would a junior engineer’s code**:
- Run tests, add missing tests.
- Review for correctness, readability, error handling.
- Check time/space complexity.
- Validate against real inputs, including boundary cases.
5. **Integrate via normal SDLC**: PR, code review, linters, CI, security checks.
6. **Document decisions**: why this approach, what was verified.
### 3) Benefits (quantify if possible)
Mention measurable or observable impacts:
- Faster iteration on boilerplate and tests.
- Better coverage of edge cases (when used to brainstorm cases).
- Improved onboarding/learning for unfamiliar libraries.
- Reduced context-switching (summarizing logs, explaining unfamiliar code).
If you can, add a metric: “cut test-writing time by ~30%” or “reduced PR cycle time.”
### 4) Downsides / risks (call them out explicitly)
Show you understand real failure modes:
- **Hallucinations / subtle bugs** (especially off-by-one, concurrency, null handling).
- **Security/privacy**: leaking secrets, PII, proprietary code into external tools.
- **IP/license risk**: uncertain provenance of generated code.
- **Maintainability**: inconsistent style, over-engineering, unreadable abstractions.
- **Over-reliance**: degraded debugging skills or shallow understanding.
### 5) How you mitigate
Concrete mitigations are what differentiates strong candidates:
- **Policy compliance**: only approved tools; no secrets/PII in prompts; sanitize inputs.
- **Verification-first mindset**: tests, property-based tests where appropriate, negative tests.
- **Security review**: dependency checks, SAST, threat modeling for sensitive areas.
- **Code ownership**: treat AI output as untrusted; you are accountable.
- **Style/consistency**: enforce formatter/linter; keep diffs small; refactor for clarity.
- **Prompt discipline**: provide interfaces and examples; request minimal changes.
### 6) Improvements you’d propose (practical and product-minded)
Offer 2–4 concrete improvements:
- **Better grounding in repository context** with citations: “show me exactly which files/functions informed this output.”
- **Built-in validation loops**: auto-generate tests + run them; highlight failing cases.
- **Security guardrails**: secret detection, PII redaction, policy warnings.
- **Deterministic change sets**: structured outputs (patch format), smaller diffs, better refactor safety.
- **Evaluation and observability**: quality metrics on suggestions, feedback capture per team.
### Example answer outline (you can adapt)
“I use an approved internal AI assistant mainly for boilerplate, test scaffolding, and to brainstorm edge cases. My workflow is to specify constraints, request a minimal solution, and then verify it through unit tests and code review like any other change. It helps me move faster on repetitive tasks and improves test coverage, but I’m careful about hallucinations and security—no secrets/PII in prompts, and I avoid using it for auth/payment logic without thorough verification. I’d improve tools by adding citations to repo context and integrating automatic test execution so suggestions are validated before they reach a PR.”