I started with the UI layer and worked backwards, which felt natural but I think I spent too long on component structure before touching the backend.
Start by clarifying requirements and scale, then present a high-level architecture that separates frontend and backend concerns, and finally dive into key components like streaming responses, state management, and scalability. Emphasize trade-offs and justify your choices based on OpenAI's specific needs.
Pro tip: Focus on the unique challenges of a ChatGPT-style app, such as real-time streaming, conversation state, and handling long-lived connections, rather than generic web app architecture. Show awareness of cost and latency implications at scale.
Ask questions to understand expected user load, latency requirements, conversation persistence, and whether it's a public or internal tool. This sets the stage for architectural decisions.
Sketch the main components: frontend (React/Next.js), backend (API gateway, auth, conversation service, model inference service), and data stores (conversation history, user data). Mention CDN, load balancers, and caching.
Detail the frontend: component structure (chat window, input, sidebar), state management (Redux/Zustand/Context), and handling streaming responses via WebSockets or Server-Sent Events (SSE). Discuss optimistic UI and error handling.
Explain the backend: API endpoints (REST/GraphQL), authentication (JWT/OAuth), conversation management (CRUD for chats), and integration with the model inference service. Highlight streaming architecture and rate limiting.
Discuss scaling strategies: horizontal scaling of stateless services, database sharding, caching, and using message queues for async processing. Address trade-offs like consistency vs. availability, and cost vs. performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining both technologies and their core differences (unidirectional vs bidirectional). Then systematically compare them across the five dimensions, highlighting trade-offs and when each is preferable. Conclude with a recommendation for streaming tokens to the browser, considering the specific constraints of LLM token streaming.
Pro tip: Mention that for token streaming, SSE is often sufficient and simpler, but WebSockets offer bidirectional communication that could be useful for interactive features like stopping generation or sending user feedback mid-stream. Also, note that HTTP/2 and HTTP/3 can mitigate some SSE limitations like connection limits.
Briefly explain SSE (unidirectional, text-based, over HTTP) and WebSockets (bidirectional, full-duplex, over TCP). Highlight that SSE is simpler and uses standard HTTP, while WebSockets require a protocol upgrade.
For each dimension (latency, reliability, backpressure, reconnection, browser support), discuss how SSE and WebSockets differ. For example, latency is similar but WebSockets may have lower overhead; reliability: SSE has built-in reconnection, WebSockets need manual; backpressure: both lack native support but can be implemented; reconnection: SSE automatic, WebSockets manual; browser support: SSE widely supported except IE, WebSockets universal.
Relate the trade-offs to streaming tokens: SSE is simpler, works with existing HTTP infrastructure, and auto-reconnects, but is unidirectional and has connection limits per domain. WebSockets are bidirectional, lower latency, but more complex and require handling reconnection and backpressure manually.
Suggest SSE for most token streaming scenarios due to simplicity and auto-reconnection, unless bidirectional communication is needed (e.g., for interactive AI features). Mention that WebSockets might be better for low-latency, high-frequency updates or when client needs to send data mid-stream.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered API key auth via headers, short-lived JWT for client sessions, and a token bucket for rate limiting.
Start by clarifying the requirements and constraints (e.g., expected traffic, latency, security needs), then walk through a layered architecture covering authentication, rate limiting, conversation state, and streaming. Emphasize trade-offs and best practices for each component, and conclude with how you would test and monitor the integration.
Pro tip: Mention that you would use exponential backoff with jitter for retries on rate limit errors, and that you would store conversation state in a way that allows for easy scaling and persistence, such as a distributed cache with TTL or a database with session IDs.
Ask about expected request volume, latency requirements, security policies, and whether the conversation state needs to be persisted long-term. This ensures your design aligns with the actual needs.
Use API keys or OAuth tokens for authentication, and ensure they are stored securely (e.g., environment variables, secret managers). Implement scopes or roles if different access levels are needed.
Apply client-side rate limiting to avoid hitting API limits, and handle 429 responses with exponential backoff and jitter. Consider using a token bucket or leaky bucket algorithm.
Store conversation history in a session store (e.g., Redis, DynamoDB) keyed by a session ID. Decide on TTL and whether to persist indefinitely, and ensure the state is updated after each API call.
Use server-sent events (SSE) or chunked transfer encoding to stream tokens. Parse the stream incrementally, handle partial tokens, and ensure the UI updates in real-time. Also, consider error handling for stream interruptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on mid-stream failures specifically, since once you've started streaming a response you can't really retry transparently.
Start by clarifying the pipeline stages and failure modes, then describe a layered error handling strategy with idempotent retries and backoff. Emphasize trade-offs between latency, cost, and reliability, and how you'd monitor and adapt the strategy.
Pro tip: Demonstrate maturity by discussing how you'd handle partial failures and ensure exactly-once semantics without sacrificing latency, and mention circuit breakers to prevent cascading failures.
Ask clarifying questions about the pipeline architecture (e.g., streaming sources, inference service, output sinks) and identify potential failure points at each stage.
Categorize errors (transient vs. permanent) and specify handling: retries for transient, dead-letter queues for permanent, and logging/alerting for all.
Describe retry logic with exponential backoff and jitter, max retry limits, and idempotency keys to avoid duplicate processing.
Discuss trade-offs between latency, cost, and reliability, and how you'd monitor retry rates, error rates, and system health to adjust strategy.
Explain how to handle partial failures, ensure exactly-once semantics, and use circuit breakers to prevent cascading failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structured logs per request with trace IDs, latency histograms on time-to-first-token and total generation time, and error rate metrics by model endpoint.
Start by clarifying the system's architecture and critical user journeys, then propose a unified observability strategy that correlates logs, metrics, and traces. Emphasize how this strategy enables proactive detection, rapid debugging, and data-driven decisions, and discuss trade-offs and tooling choices.
Pro tip: Show maturity by discussing how you'd balance observability costs with value, and how you'd use observability data to drive product and engineering improvements, not just firefighting.
Ask questions to understand the system's components, scale, and critical user journeys. Define what success looks like for observability (e.g., SLOs, debugging speed, cost efficiency).
Outline how you'd implement logs (structured, centralized), metrics (key business and system metrics, dashboards), and distributed tracing (instrumentation, context propagation, sampling).
Explain how you'd tie the pillars together using unique identifiers (e.g., trace IDs in logs) and unified tooling to enable seamless navigation from metrics to traces to logs.
Describe processes for alerting, anomaly detection, and continuous improvement. Discuss how you'd use observability data to inform capacity planning, feature rollouts, and incident response.
Discuss trade-offs (e.g., sampling rates, retention costs) and justify tool choices (e.g., Prometheus, Jaeger, ELK) based on requirements and scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
GPU compute is the dominant cost so you want to maximize utilization through batching requests to the model where possible.
Start by clarifying the system's scale and workload characteristics, then systematically analyze scalability bottlenecks and cost drivers across compute, storage, and networking. Discuss trade-offs between scaling approaches and cost optimization strategies, and conclude with a balanced recommendation that aligns with business goals.
Pro tip: Quantify where possible—use rough numbers (e.g., QPS, data volume, cost per request) to ground your analysis and show you think in terms of orders of magnitude. Also, mention that scalability and cost are often in tension, so the goal is to find the sweet spot for the given requirements.
Ask questions to understand expected traffic, data size, growth rate, and usage patterns (e.g., read/write ratio, peak vs. average load). This sets the context for all subsequent analysis.
Break down the system into components (e.g., compute, storage, network, database) and discuss how each scales. Mention horizontal vs. vertical scaling, sharding, replication, caching, and asynchronous processing.
Estimate costs for compute (VMs, containers, serverless), storage (object, block, database), network egress, and managed services. Highlight how costs scale with usage and where they can explode.
Present trade-offs between scalability and cost (e.g., over-provisioning vs. auto-scaling, strong vs. eventual consistency). Suggest optimizations like caching, compression, tiered storage, and reserved instances.
Provide a concise summary of the key considerations and propose a balanced approach that meets scalability needs while managing costs, possibly with monitoring and iterative improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.