I started with the happy path and that was probably a mistake.
Start by clarifying the requirements and constraints of the file upload feature, such as supported file types, size limits, and expected scale. Then, outline a high-level architecture covering the upload flow, storage, processing, and integration with the AI chat. Finally, dive into key technical decisions like chunked uploads, security, and trade-offs between synchronous and asynchronous processing.
Pro tip: Emphasize how the design handles large files and ensures a seamless user experience, as these are common pain points in AI chat applications. Also, discuss how you would monitor and handle failures gracefully to maintain reliability.
Ask questions to understand the expected file types, maximum file sizes, number of concurrent uploads, and any latency or throughput requirements. This ensures the design meets the actual needs.
Sketch the main components: client, API gateway, upload service, storage (e.g., object store), processing pipeline (e.g., for text extraction or embedding), and integration with the AI chat backend. Explain the flow from upload to AI response.
Detail critical aspects such as chunked/resumable uploads for large files, virus scanning, metadata storage, and asynchronous processing with message queues. Discuss how to handle failures and retries.
Compare options like direct-to-S3 uploads vs. proxying through your servers, synchronous vs. asynchronous processing, and cost implications. Explain how the design scales with increasing load.
Recap the proposed design, highlighting how it meets the requirements and handles edge cases. Mention potential future improvements or monitoring strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.