This one tripped me up more than I expected.
Walk through your prompt design as a series of deliberate engineering decisions, explaining the goal, constraints, and trade-offs you considered. Emphasize how you iterated based on results and adapted to ambiguity, showing a systematic and reflective process.
Pro tip: Frame your prompt as a prototype for a production system: discuss how you'd measure its performance and what you'd change to make it robust and scalable. This shows you think beyond the immediate task and align with Discord's engineering culture.
State the specific goal you wanted the AI to achieve and why it mattered for the task or user.
Describe the technical, time, or data constraints you faced and any assumptions you made about the AI's behavior.
Detail the key elements of your prompt (e.g., structure, examples, tone) and the reasoning behind each, linking to trade-offs like clarity vs. flexibility.
Discuss how you tested the prompt, what you learned from failures or ambiguities, and how you refined it.
Relate your approach to broader software engineering practices like modularity, testing, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more confident here since I'd actually looked at the dependencies.
Start by restating the use case and the AI's library choices to show you understand the context. Evaluate each library against criteria like performance, scalability, and fit for Discord's real-time, high-throughput environment. Conclude with a balanced verdict, acknowledging trade-offs and suggesting alternatives if needed.
Pro tip: Mention that library appropriateness depends on specific constraints like latency, concurrency, and maintenance overhead—showing you consider non-functional requirements, not just features.
Summarize the problem the AI was solving, including scale, latency, and integration needs specific to Discord.
List the libraries the AI selected and their core purposes, noting any obvious mismatches with the use case.
Assess each library on performance, scalability, community support, security, and compatibility with Discord's tech stack.
Propose better-suited libraries if applicable, explaining why they would improve the solution.
State whether the libraries are appropriate overall, highlighting trade-offs and any conditions that would change your assessment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The AI had added some error handling I didn't ask for.
Acknowledge that AI-generated extras can be valuable but must be evaluated against the original requirements and project constraints. Share a specific example where you assessed such additions, weighing usefulness against complexity, and describe how you decided to keep, modify, or discard them.
Pro tip: Emphasize that you always validate AI suggestions through code review and testing, and that you consider maintainability and team velocity—not just immediate functionality—when deciding what to keep.
Restate what was actually requested and why, to establish a baseline for evaluating any extras.
Evaluate the addition on criteria like user value, alignment with product vision, and whether it solves a real problem.
Consider costs: added complexity, maintenance burden, performance impact, and potential technical debt.
Make a call—keep, adapt, or remove—and explain how you communicated the decision to stakeholders.
Share what you learned and how it improved your process for integrating AI suggestions in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard enough question but the live PuTTY context made it feel more immediate.
Start by clarifying the code's purpose, inputs, outputs, and dependencies to understand what needs testing. Then outline a layered testing strategy covering unit, integration, and end-to-end tests, prioritizing critical paths and edge cases. Conclude by discussing trade-offs like test coverage vs. maintenance cost and how you'd measure success.
Pro tip: Emphasize testing for failure modes and edge cases first, as they often reveal the most critical bugs. Also, mention how you'd use mocks and stubs to isolate components, especially in a microservices environment like Discord's.
Ask questions to understand the code's functionality, expected behavior, and constraints. Identify what aspects are most critical to test based on business impact.
Outline a testing pyramid: unit tests for individual functions, integration tests for component interactions, and end-to-end tests for user flows. Prioritize based on risk and complexity.
Enumerate normal cases, edge cases, and error conditions. Consider boundary values, invalid inputs, and failure scenarios like network issues or database errors.
Select appropriate testing tools (e.g., JUnit, pytest, Jest) and mocking libraries. Discuss how to automate tests and integrate with CI/CD pipelines.
Explain how you'd balance test coverage with development speed. Define success metrics like defect escape rate or test execution time, and how to maintain tests over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Probably the most interesting question of the whole round.
Walk through a concrete example of adding a feature, emphasizing how you craft a detailed prompt with context, constraints, and acceptance criteria, then describe a rigorous verification process that includes automated tests, manual testing, and code review. Highlight the importance of understanding the AI's limitations and iterating on both the prompt and the code.
Pro tip: Treat the AI as a junior engineer: give it clear requirements, review its work critically, and never merge without thorough testing. Also, mention that you'd version control the prompt alongside the code for reproducibility.
Clearly specify the feature's purpose, inputs, outputs, edge cases, and performance expectations. Break it down into small, testable units.
Include context (e.g., codebase, language, frameworks), constraints (e.g., style, libraries), and examples. Ask for tests and documentation.
Inspect the AI's output for correctness, security, and adherence to standards. Refine the prompt or manually adjust as needed.
Run unit, integration, and end-to-end tests. Ensure coverage for edge cases and performance benchmarks.
Manually test the feature in a realistic environment and have peers review the code for quality and maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.