The data structure part I knew cold, hash map pointing to nodes in a doubly linked list, move on access, evict from tail.
Start by clarifying requirements and constraints, then explain the hash map + doubly linked list design for O(1) operations. Walk through the get and put logic, emphasizing edge cases and how you'd collaborate with an AI assistant to implement and test the solution.
Pro tip: Treat the AI assistant as a pair programmer: articulate your design decisions out loud, ask it to generate boilerplate or tests, but critically review its output for correctness and edge cases. This demonstrates both technical depth and effective collaboration.
Ask about cache capacity, expected operations, thread safety, and whether the AI assistant should be used for implementation or just review. Confirm that O(1) time complexity is required for both get and put.
Describe using a hash map for O(1) access to nodes and a doubly linked list to maintain recency order. The list head represents most recently used, tail least recently used.
For get: if key exists, move node to head and return value; else return -1. For put: if key exists, update value and move to head; else insert new node at head, and if capacity exceeded, remove tail node and its map entry.
Explain how you'd use the AI to generate initial code, suggest test cases, or identify edge cases. Emphasize verifying the AI's suggestions and iterating together.
Mention handling capacity 0 or 1, duplicate keys, and thread safety if needed. Propose writing unit tests for get/put sequences and eviction order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Emphasize that you treat AI-generated code like any other external code: you don't trust it blindly. Walk through a systematic verification process that starts with understanding the code's intent, then rigorously testing it with edge cases and automated tests. Highlight your ability to adapt to unfamiliar code and ensure correctness.
Pro tip: Mention that you often use property-based testing or fuzzing to uncover edge cases you might not think of, and that you always review AI-generated code for security and performance pitfalls, not just correctness.
Before testing, clarify what the code is supposed to do, its inputs/outputs, and how it fits into the larger system. Read any accompanying documentation or comments, and if unclear, ask the AI to explain its reasoning.
Scan for obvious issues like off-by-one errors, unhandled exceptions, or incorrect assumptions. Check for code smells, security vulnerabilities, and adherence to coding standards.
Start with happy path tests to confirm basic correctness. Then, systematically add tests for edge cases: empty inputs, nulls, boundary values, large inputs, and invalid types.
Use property-based testing to generate random inputs and verify invariants. Consider mutation testing to assess test suite quality, and fuzzing for unexpected crashes.
Run the code in a staging environment with integration tests. Monitor performance and error rates in production, and be ready to roll back if issues arise.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting part of the whole round.
Walk through a concrete example of a well-defined engineering task, such as adding a new API endpoint or refactoring a module. Explain how you break the task into smaller, testable units and craft prompts that provide clear context, constraints, and expected output format. Emphasize iterative refinement and validation of the AI's output against tests and code review standards.
Pro tip: Always include the relevant code context, such as function signatures, data models, and existing patterns, in your prompt, and ask the AI to explain its reasoning or generate tests alongside the code. This not only improves output quality but also helps you catch subtle issues early.
Clearly articulate the engineering task, including inputs, outputs, constraints, and success criteria. Break it into smaller, independent subtasks if possible.
Collect relevant code snippets, documentation, and examples of desired patterns. Include this context in your prompt to ground the AI's response.
Write a prompt that specifies the role, task, constraints, and expected format. Use examples or templates to guide the AI toward the desired output.
Review the AI's output, provide feedback, and ask for revisions. Break down complex requests into multiple rounds of interaction.
Test the generated code against unit tests, perform code review, and ensure it adheres to team standards before integrating.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.