The problem itself was fine, not especially tricky.
Start by clarifying the requirements and scope of the rule parser, then outline a structured approach that combines AI assistance with traditional parsing techniques. Emphasize iterative development, testing, and trade-offs between using AI for generation versus manual coding.
Pro tip: Demonstrate awareness of AI limitations by discussing how you would validate and test AI-generated code, and mention specific tools like GitHub Copilot or ChatGPT for generating parser code.
Ask questions to understand the rule format, expected inputs/outputs, performance needs, and error handling. This shows you avoid assumptions and scope the problem effectively.
Decide between writing a parser from scratch (e.g., recursive descent) or using a parser generator (e.g., ANTLR). Consider using AI to generate initial code or grammar.
Use AI tools to generate boilerplate, suggest grammar rules, or translate natural language rules into code. But plan to review and refine the output.
Write unit tests for edge cases, integrate the parser, and iterate based on test results. Use AI to suggest test cases or identify potential bugs.
Discuss pros and cons of AI-assisted development: speed vs. correctness, maintainability, and learning curve. Highlight when to rely on AI and when to code manually.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.