Start by clarifying requirements and scale, then walk through the end-to-end flow from client to backend, highlighting key components like quota tracking, streaming, and API design. Emphasize trade-offs and justify your choices with reasoning about scalability, reliability, and user experience.
Pro tip: Focus on the unique challenges of streaming responses and quota enforcement at scale, and discuss how you would handle edge cases like partial failures or quota exhaustion mid-stream. Demonstrating awareness of these nuances shows depth beyond a generic design.
Ask questions to understand expected user base, request volume, latency requirements, and free-tier limits. Establish assumptions to guide the design.
Sketch the main components: clients, API gateway, authentication, quota service, conversation service, model inference, and streaming infrastructure. Explain data flow.
Define RESTful or WebSocket endpoints for chat, including streaming via SSE or WebSockets. Describe client-side handling of streaming, retries, and quota errors.
Detail how to track usage per user (e.g., token counts, request counts) using a fast datastore like Redis, and enforce limits at the API gateway or a dedicated service. Discuss atomicity and race conditions.
Explain deployment topology: load balancers, stateless services, model serving with GPU pools, message queues for async processing, and autoscaling. Address fault tolerance and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about a centralized quota service with a counter per user, decrement on each request, and a 429-style error with a specific error code distinguishing quota exhaustion from rate limiting.
Start by clarifying the requirements and assumptions, such as the definition of a free tier and the expected scale. Then, walk through the design in layers: quota tracking, enforcement, error handling, and upgrade prompts. Emphasize reliability, user experience, and scalability, and discuss trade-offs of different approaches.
Pro tip: Show that you consider the user experience even when they hit limits—make the error message helpful and the upgrade path seamless. Also, mention the importance of monitoring and alerting to detect abuse or bugs in the quota system.
Ask questions to understand the scope: What defines the free tier? Is it per user, per API key, or per IP? What is the expected request volume? How strict should enforcement be?
Choose a storage solution (e.g., Redis, database) for tracking usage. Consider using a sliding window or fixed window counter, and discuss trade-offs like accuracy vs. performance. Ensure atomic increments to avoid race conditions.
Check quota before processing each request. If exceeded, return a clear error (e.g., HTTP 429) with a descriptive message and relevant headers (e.g., X-RateLimit-Remaining). Ensure the error response is consistent and informative.
Include a link or call-to-action in the error response or UI to guide users to upgrade. Consider different channels: API response, email notification, dashboard banner. Make the upgrade process frictionless.
Set up monitoring for quota usage and errors. Test edge cases like concurrent requests and quota resets. Be prepared to adjust limits based on user feedback and business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's core use case and constraints, then walk through your API design decisions in a logical order: request/response structure, error handling, rate limiting, and retry/idempotency. Emphasize trade-offs and how your choices align with OpenAI's scale, reliability, and developer experience goals.
Pro tip: Tie every design decision back to real-world failure modes and client impact—e.g., how idempotency keys prevent duplicate charges or how rate limiting protects shared resources—showing you think beyond the happy path.
Ask about expected traffic, latency, consistency needs, and client types to ground your design. This shows you avoid over-engineering and tailor solutions to actual needs.
Define resource-oriented endpoints, use JSON with clear field naming, and include metadata like request IDs. Explain how you'd version the API and handle pagination for lists.
Use standard HTTP status codes (e.g., 400, 401, 429, 500) and a consistent error body with machine-readable codes and human-readable messages. Discuss how to avoid leaking sensitive info.
Choose a strategy (e.g., token bucket, sliding window) based on fairness and burst tolerance. Explain how to communicate limits via headers (e.g., X-RateLimit-Limit) and handle 429 responses gracefully.
Require idempotency keys for non-idempotent operations (e.g., POST) and store them server-side with a TTL. Describe retry logic with exponential backoff and jitter, and how to detect and deduplicate retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around the three sub-problems: retries, error handling, and context management. For each, explain the trade-offs and propose concrete strategies, such as exponential backoff with jitter for retries, distinguishing between retryable and non-retryable errors, and summarizing or truncating conversation history. Emphasize idempotency and user experience throughout.
Pro tip: Mention that retries should be idempotent and that you should use a unique request ID to deduplicate on the server side. Also, highlight that context window management is not just about truncation—consider summarization, prioritization, and even external memory stores.
Ask about the expected error rates, latency requirements, and whether the conversation is stateful. This shows you think about the problem in context.
Propose exponential backoff with jitter, set a maximum retry limit, and ensure retries are idempotent. Discuss when to retry (e.g., network errors, 5xx) vs. when not to (e.g., 4xx).
Categorize errors: transient (retry), client errors (fix request), and server errors (retry with backoff). For non-retryable errors, provide clear feedback to the user or fallback.
When context exceeds window, use strategies like summarization, truncation of oldest messages, or retrieval of relevant past messages. Discuss trade-offs between losing context and staying within limits.
Suggest logging retries and context management decisions to monitor effectiveness and adjust strategies over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by comparing streaming and non-streaming delivery in terms of latency, user experience, and resource usage, then outline a concrete SSE implementation with a fallback mechanism. Emphasize trade-offs and how to detect client support to ensure graceful degradation.
Pro tip: Mention that SSE is ideal for one-way server-to-client streaming but requires careful handling of connection limits and reconnection logic; also note that fallback should be automatic and seamless to the user.
Discuss how streaming reduces perceived latency and improves UX for long-running tasks, while non-streaming is simpler but can cause timeouts and higher memory usage.
Explain why SSE is a good fit: it's built on HTTP, supports automatic reconnection, and is simpler than WebSockets for unidirectional data.
Describe setting up an SSE endpoint with proper headers (Content-Type: text/event-stream), sending events with data, and handling client disconnects.
Outline how to detect lack of SSE support (e.g., via feature detection or Accept header) and fall back to polling or a single non-streaming response.
Mention considerations like connection limits, load balancing, and using a message queue to decouple producers from consumers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I drew out gateway, model fleet, conversation store, and quota service.
Start by clarifying the system's scale and requirements, then describe the backend as a layered architecture: edge/gateway, stateless services, model serving, and storage. Walk through a single request end-to-end, highlighting how components interact and the trade-offs at each layer.
Pro tip: Emphasize statelessness, caching, and asynchronous processing to show you understand how to build scalable, resilient systems. Mention specific OpenAI-scale challenges like GPU scheduling and multi-tenant isolation.
Ask about expected traffic, latency SLAs, and data consistency needs to tailor your design. State assumptions explicitly to frame the discussion.
List the main backend services: API gateway, authentication, request orchestrator, model serving, and storage layers. Briefly describe each service's responsibility.
Walk through a typical request from client to gateway, through orchestration, to model inference, and finally to storage. Explain how services communicate (e.g., REST, gRPC, message queues).
Detail how services interact for key operations: authentication, rate limiting, model selection, inference, and logging. Highlight synchronous vs asynchronous calls.
Explain design choices like caching, sharding, and autoscaling, and their impact on latency, cost, and reliability. Mention failure handling and observability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.