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This part was less stressful than I expected.
Acknowledge that AI coding assistants can be powerful for navigating unfamiliar code, but emphasize that they are most effective when combined with traditional exploration techniques. Describe a balanced workflow: start with AI to get a high-level map, then verify and dive deeper manually to build true understanding.
Pro tip: Mention that AI assistants can hallucinate or miss context, so always cross-check their suggestions with the actual code and tests. Also, highlight that explaining the code back to the AI (or a colleague) is a great way to solidify your understanding.
Use the AI assistant to ask broad questions like 'Where is the logic for X?' or 'What does class Y do?' to get a quick overview and identify key files or functions.
Open the suggested files, read the code, and trace the logic yourself. Use IDE features like 'Find Usages' and 'Go to Definition' to understand dependencies and flow.
Once you've identified a specific class or function, ask the AI to explain its responsibilities, inputs/outputs, and edge cases. Compare with your own reading.
Look at unit tests, integration tests, and any available docs to confirm the AI's explanations and uncover additional context.
Summarize your findings, perhaps by writing a short note or explaining it to someone else. If possible, make a small change and run tests to validate your understanding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once I figured out where the relevant logic was.
Start by exploring the codebase to understand its structure, entry points, and the specific simulation logic. Identify the smallest change that achieves the goal, then implement it with tests to verify correctness. Communicate your reasoning and trade-offs throughout.
Pro tip: Before modifying code, run the simulation to observe its current behavior and establish a baseline; this helps you catch unintended side effects early. Also, use the debugger or logging to trace execution paths rather than guessing.
Quickly scan the repository structure, README, and key files to locate the simulation code and understand its purpose. Identify the main entry point and how the simulation is invoked.
Read the specific functions or classes involved in the modification, tracing data flow and dependencies. Clarify any assumptions by checking tests or documentation.
Determine the smallest possible edit that achieves the desired behavior, considering edge cases and potential side effects. Prefer modifying existing code over adding new abstractions.
Make the change, then run existing tests and add a focused test if needed. Manually run the simulation to confirm the modification works as intended.
Explain what you changed, why, and any trade-offs or uncertainties. Mention how you would validate further or what you learned about the codebase.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the simulation's goals and existing metrics, then propose a metric that captures a key efficiency dimension (e.g., on-time performance, resource utilization, or throughput) and justify it with clear reasoning. Outline how you would implement it in the code, including data collection, calculation, and integration points, and discuss trade-offs and validation.
Pro tip: Tie your metric to a business outcome (e.g., reducing wait times or fuel costs) to show product thinking, and mention how you'd validate it against real-world data or A/B tests to ensure it drives the right behavior.
Ask clarifying questions about the simulation's purpose, existing metrics, and what 'efficiency' means in this context. Identify stakeholders and key performance indicators.
Define a metric that addresses a gap or improves on existing ones, and explain why it's the right choice (e.g., aligns with business goals, actionable, measurable).
Outline how to compute the metric from simulation data, including data sources, calculation logic, and where to integrate it in the codebase (e.g., new module, existing class).
Discuss potential trade-offs (e.g., complexity vs. insight, performance impact) and how you would validate the metric (e.g., backtesting, sensitivity analysis).
Describe the integration process, including testing, monitoring, and how you would iterate based on feedback or changing requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.