The velocity angle is what threw me a bit.
Start by thoroughly understanding the existing codebase, tests, and rule format to infer the expected node behavior. Use AI coding tools to generate initial implementations, then critically review and refine them to ensure correctness and edge-case handling. Finally, write additional tests that cover untested paths and validate the integration of nodes with the workflow engine.
Pro tip: Treat AI-generated code as a first draft: always verify its logic against the tests and add your own tests to catch subtle bugs, especially around error handling and concurrency. This demonstrates you don't blindly trust AI and can ensure production-quality code.
Read the existing code, especially the rule parser, workflow engine, and all tests, to infer the contract for node implementations. Identify the rule format and how nodes are invoked.
Use AI coding tools to draft the missing node implementations based on the inferred contract. Prompt the AI with clear context and examples from the tests.
Critically evaluate the generated code for correctness, edge cases, and alignment with the existing architecture. Fix any issues and ensure all existing tests pass.
Create new tests that cover scenarios not addressed by the existing tests, such as error conditions, boundary values, and integration between nodes. Ensure these tests pass.
Run the full test suite to confirm everything works. Document any assumptions or design decisions made during implementation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this almost as a debrief question after I finished coding.
Emphasize that you treat AI-generated code as a first draft and apply the same rigorous review process as any other code, focusing on correctness, security, and maintainability. Highlight specific steps you take to validate and improve the code before opening a PR, and tie it back to DoorDash's engineering culture of ownership and quality.
Pro tip: Mention that you often add comments or commit messages noting which parts were AI-assisted, so reviewers know where to focus—this builds trust and shows transparency.
Read through the AI-generated code line by line to ensure you understand its logic and intent. Run it locally and test edge cases to confirm it behaves as expected.
Scan for common vulnerabilities (e.g., injection, data leaks) and assess performance implications, especially in DoorDash's high-scale environment. Refactor or rewrite any problematic sections.
Apply your team's style guide, remove redundant or overly complex code, and add meaningful comments. Make sure it integrates well with the existing codebase.
Add unit and integration tests to cover the new code, ensuring it meets the same coverage standards as manually written code. Verify that all tests pass.
Summarize the changes, note any AI assistance, and highlight areas needing reviewer attention. Link to relevant tickets or documentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.