This was the whole interview, basically one giant open-ended prompt.
Start by clarifying requirements and scale, then present a high-level architecture that separates concerns: frontend (React with state management), backend (API gateway, orchestration, data stores), and streaming infrastructure. Focus on how streaming responses are handled end-to-end, how parameters and presets are managed, and how data is organized for scalability and low latency.
Pro tip: Emphasize trade-offs and failure modes, such as handling streaming interruptions, rate limiting, and cost controls, to show you think beyond the happy path. Also, mention observability and monitoring as key components of the architecture.
Ask about expected traffic, latency requirements, and key features (e.g., real-time streaming, preset sharing). This ensures the design meets actual needs and demonstrates thoroughness.
Sketch the main components: frontend (React SPA), backend (API gateway, orchestration service, model inference service), data stores (for presets, user data, logs), and streaming infrastructure (WebSockets or SSE). Explain how they interact.
Describe how the frontend manages state (e.g., Redux, Zustand, or React Context) for parameters, conversation history, and streaming responses. Explain how streaming data is received (e.g., SSE) and incrementally rendered.
Detail the data models for users, presets, and conversations. Discuss storage choices (e.g., PostgreSQL for relational data, Redis for caching) and how presets are saved, retrieved, and shared.
Explain how parameter controls (temperature, max tokens, etc.) are exposed in the UI and passed to the backend. Discuss API endpoints for completions, streaming, and preset management, including validation and rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to a relational schema and they seemed fine with it, but I didn't think through the versioning angle for presets until they pushed back.
Start by clarifying the core entities and their relationships, then propose a normalized schema with appropriate indexes and constraints. Discuss how to handle versioning, access control, and scalability, and justify trade-offs between flexibility and performance.
Pro tip: Emphasize idempotency and auditability for prompts and presets, and mention how you'd handle schema evolution without breaking existing clients—this shows you think about production realities.
Define the main objects: users, prompts, model parameters, presets, and their associations. Clarify cardinality (e.g., one user has many presets, a preset references a prompt and parameters).
Propose tables/collections with primary keys, foreign keys, and indexes. Include fields like prompt text, parameter name/value pairs, and preset metadata. Enforce uniqueness and referential integrity.
Explain how to track changes to prompts and presets (e.g., version columns, history tables). Discuss soft deletes and timestamps for audit trails.
Describe how to scope data per user or organization, including row-level security or tenant IDs. Mention sharing and permission models for presets.
Compare SQL vs NoSQL, denormalization for read performance, caching strategies, and sharding. Justify choices based on expected query patterns and scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements such as latency, scale, and client constraints. Then describe an end-to-end architecture using streaming protocols like Server-Sent Events (SSE) or WebSockets, covering how the model's token stream is chunked and delivered to the frontend. Finally, discuss trade-offs and optimizations for real-time performance and reliability.
Pro tip: Mention that OpenAI's own API uses SSE for streaming, and highlight the importance of handling backpressure and connection drops gracefully to ensure a smooth user experience.
Ask about expected latency, number of concurrent users, client types (web, mobile), and whether partial responses are acceptable. This ensures your solution aligns with the actual needs.
Compare SSE, WebSockets, and HTTP chunked transfer. Explain why SSE is often ideal for one-way server-to-client streaming, but WebSockets may be needed for bidirectional communication.
Describe how the backend receives the stream from the GPT-3 API (e.g., via HTTP streaming), processes chunks, and forwards them to the frontend. Include load balancing, connection management, and scaling considerations.
Explain how the frontend consumes the stream (e.g., using EventSource for SSE or WebSocket API), updates the UI incrementally, and handles errors or reconnections.
Discuss trade-offs like latency vs. throughput, buffering strategies, backpressure, and fallback mechanisms. Mention monitoring and testing for real-time performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, such as the complexity of the UI, real-time needs, and scalability. Then propose a state management architecture that separates concerns (e.g., local vs. global state) and describe how you would handle user interactions with debouncing, optimistic updates, and error handling. Finally, discuss trade-offs between different approaches and justify your choices based on the context.
Pro tip: Demonstrate awareness of OpenAI's specific challenges, such as handling streaming responses and rate limiting, by mentioning how you would manage partial updates and backpressure in the UI. Also, emphasize the importance of accessibility and responsiveness in a high-traffic application.
Ask questions to understand the scope: expected number of parameters, frequency of updates, need for real-time feedback, and whether the UI is for internal or external users. This shows you don't jump to solutions without context.
Decide between local component state, global state (e.g., Redux, Zustand), or server state (e.g., React Query). Explain how you'd handle form state for model parameters and prompt input, considering performance and developer experience.
Describe how you'd manage parameter changes (e.g., debouncing, validation) and prompt submission (e.g., optimistic UI, loading states, error handling). Mention techniques like throttling for sliders and disabling submit during in-flight requests.
Explain how you'd handle streaming responses from the API, such as using WebSockets or server-sent events, and updating the UI incrementally. Discuss state synchronization and avoiding race conditions.
Compare approaches (e.g., controlled vs. uncontrolled components, global vs. local state) and justify your choices. Mention performance optimizations like memoization, virtualization, and code splitting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.