Be honest and specific about your AI tool usage, framing it as a productivity enhancer rather than a crutch. Emphasize how you use AI to handle routine tasks so you can focus on complex problem-solving, and mention any safeguards you apply to ensure code quality.
Pro tip: Show that you treat AI as a junior collaborator: you review its output critically, test thoroughly, and never blindly trust it. This demonstrates maturity and aligns with Expedia's emphasis on technical trade-offs and adaptability.
Begin by confirming whether you use AI coding assistants, and if so, which ones (e.g., GitHub Copilot, ChatGPT). Be transparent about frequency—daily, weekly, or occasionally.
List specific tasks where AI adds value, such as writing boilerplate code, generating unit tests, debugging, or learning new APIs. Avoid vague statements like 'I use it for everything.'
Detail how AI fits into your development process: when you invoke it, how you validate its suggestions, and how you ensure it doesn't introduce security or performance issues.
Discuss the productivity gains you've experienced, but also acknowledge limitations—e.g., AI may produce incorrect or insecure code, so human oversight is essential.
Tie your answer to Expedia's context: how AI helps you deliver faster, experiment more, and adapt to changing requirements, while maintaining high standards for code quality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a clear, repeatable workflow that balances AI speed with human oversight. Emphasize how you validate AI-generated code, integrate it into your team's development process, and maintain quality standards. Tailor the workflow to Expedia's context by mentioning collaboration, code reviews, and agile practices.
Pro tip: Highlight that you treat AI as a junior pair programmer: you give it clear context, review its output critically, and never merge without thorough testing. This shows maturity and mitigates risks like hallucinations or security flaws.
Start by defining the problem, constraints, and expected output format. Provide relevant code snippets, error messages, or documentation to give the AI sufficient context.
Critically evaluate the generated code for correctness, edge cases, and style. Refine the prompt or manually adjust the code as needed, treating the AI as a starting point.
Run unit tests, integration tests, and linters to ensure the code works within your local environment. Fix any issues before committing.
Write a clear commit message explaining the change and its AI-assisted origin if relevant. Open a PR, ensuring it follows team conventions and includes a description of testing done.
Engage with reviewers, address feedback, and ensure the final code meets quality gates. Use the review as a learning opportunity to improve future AI interactions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose one or two specific, measurable examples where AI coding tools made a real difference in your work, and structure each as a mini-story (situation, tool, outcome). Balance the benefits with honest trade-offs to show you evaluate tools critically rather than hype them.
Pro tip: Quantify the impact where possible (e.g., 'cut boilerplate time by 40%') and mention a case where AI output needed significant correction—this shows you use these tools with judgment, not blind trust.
Select a specific project or task where you used an AI coding tool (e.g., Copilot, ChatGPT, Cursor) and can describe the before-and-after clearly.
State which dimension the benefit falls under—speed, code quality, learning, or collaboration—so your answer stays focused and easy to follow.
Describe the tangible result: time saved, bugs caught, tests generated, or a concept you learned faster, ideally with a rough metric.
Mention a limitation or risk you navigated (e.g., hallucinated APIs, over-reliance, security concerns) and how you mitigated it.
Tie the benefit back to how it would help you contribute at Expedia—faster iteration on travel features, better code reviews, or scaling with a large codebase.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The question listed so many categories that I just tried to hit all of them, which meant I went surface-level on each.
Acknowledge the value of AI-generated code while emphasizing a risk-aware mindset. Structure your answer around the main risk categories (hallucinations, security, IP, over-reliance) and explain how you mitigate each in practice. Tie your approach to Expedia's context by highlighting the importance of code reviews, testing, and secure development practices.
Pro tip: Emphasize that AI is a tool, not a replacement for engineering judgment—always validate generated code with tests and peer reviews. Mention that you treat AI suggestions as you would a junior developer's code: helpful but requiring scrutiny.
Briefly state that AI can boost productivity but introduces unique risks that require active management.
List the main risks: hallucinations (incorrect or non-existent APIs), security vulnerabilities (injection flaws, insecure defaults), IP concerns (license contamination), and over-reliance (skill atrophy, blind trust).
For each risk, explain concrete steps you take: e.g., verifying APIs against docs, running static analysis and security scans, checking licenses, and maintaining a 'trust but verify' approach with thorough testing.
Highlight how you integrate these practices into team workflows: code reviews, pair programming, and promoting awareness of AI limitations.
Conclude that AI is a powerful assistant when used responsibly, and that vigilance ensures its benefits outweigh the risks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about code review practices and not feeding proprietary data into external tools.
Focus on concrete, habitual actions you take in your daily engineering work, such as writing tests, using feature flags, and conducting code reviews. Emphasize how these practices are embedded in your workflow, not just theoretical awareness. Use a specific example to illustrate the impact of these mitigations.
Pro tip: Quantify the impact of your risk mitigation where possible (e.g., 'reduced production incidents by 30%') and tie it to business outcomes like reliability or speed. This shows you think like an owner, not just a coder.
Briefly name a specific technical risk you've encountered, such as a risky deployment or a complex dependency. This sets the context for your mitigation actions.
Explain the day-to-day practice you use to mitigate that risk, like writing automated tests, using canary releases, or conducting thorough code reviews. Be specific about how you incorporate it into your sprint work.
Detail how this mitigation fits into your team's Agile ceremonies, such as defining acceptance criteria in planning or adding monitoring tasks to the sprint backlog. This demonstrates you don't treat risk separately from delivery.
Mention how you work with others (e.g., pair programming, design reviews) to ensure the mitigation is effective and shared across the team. This shows you value collective ownership.
Conclude with the result: how did this mitigation reduce risk, improve quality, or speed up delivery? Use metrics if possible to make it tangible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Caught me a little flat-footed because it's more of a product thinking question than an engineering one.
Pick one specific, well-scoped improvement that shows you understand both the technical and human sides of AI coding tools. Ground it in a real pain point you've observed or experienced, and explain the trade-offs and impact of your proposed change. Connect it to how it would benefit a team like Expedia's, balancing innovation with reliability and scale.
Pro tip: Frame your improvement as a hypothesis with measurable outcomes (e.g., 'reduce review time by X%') rather than a vague wish—this shows product thinking and engineering rigor. Also, acknowledge the counterargument to demonstrate you've considered trade-offs.
Choose one concrete issue with AI coding tools—such as lack of context awareness, poor test generation, or inconsistent code review suggestions—and briefly explain why it matters.
Describe your change clearly: what would you modify or add? Keep it feasible and focused, whether it's a product feature, team practice, or guardrail.
Discuss why this improvement is valuable, including how it addresses the pain point and what technical or workflow benefits it brings.
Acknowledge potential downsides, such as increased complexity, cost, or adoption challenges, and how you would mitigate them.
Tie your improvement to outcomes that matter for a company like Expedia, such as developer productivity, code quality, or faster iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.