Structure your answer around a clear development lifecycle: start with requirements and scoping, then show how you'd use an AI assistant to generate code, validate it, and integrate components. Emphasize iterative prompting, testing, and security considerations specific to vehicle data.
Pro tip: Highlight that AI-generated code must be validated against real vehicle APIs and safety standards; mention Ford's Vehicle Data APIs or similar and the importance of not blindly trusting AI output.
Clarify what telemetry data (cabin temperature, speed) is needed, the update frequency, and the target platform (mobile, web, embedded). Identify constraints like real-time processing, data privacy, and Ford's specific APIs.
Decompose the app into modules: data ingestion, processing, UI, and storage. For each, craft specific prompts for the AI assistant, including context about Ford's SDKs and desired output format.
Choose appropriate Ford APIs (e.g., FordPass Connect) or third-party telemetry SDKs. Use the AI assistant to generate boilerplate code, API calls, and data models, iterating on prompts for accuracy.
Review generated code for correctness, security, and compliance. Write unit and integration tests, mock vehicle data, and verify against API documentation. Use static analysis tools.
Stitch modules together, handle authentication, error handling, and data flow. Deploy to a test environment, monitor performance, and iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.