← Anthropic Interview Insights
Start by clarifying the tokenizer's expected behavior and edge cases, then systematically test with representative inputs to reproduce the bug. Trace the code execution to isolate the first failure, explain the root cause with a specific line and input, and propose a minimal fix with validation.
Pro tip: Demonstrate a methodical debugging process: state your hypothesis before testing, and after fixing, suggest adding a regression test to prevent recurrence. This shows engineering rigor and prevents future issues.
Ask clarifying questions about the tokenizer's expected behavior, supported inputs, and edge cases (e.g., empty string, special characters, Unicode). This ensures you understand what 'correct' means.
Design and run a few minimal test cases that cover normal and edge inputs. Identify an input that produces incorrect output and note the expected vs. actual result.
Walk through the code line by line with the failing input, using print statements or a debugger. Pinpoint the exact line where the behavior diverges from expectations.
Articulate why the bug occurs (e.g., off-by-one, incorrect condition, mishandled edge case) and suggest a concrete code change. Verify the fix with the failing test and additional cases.
Mention adding a regression test and consider if the fix introduces any performance or complexity trade-offs. Optionally, discuss alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.