I started with the obvious stuff like multi-turn chat and user accounts, which felt fine.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates concerns (API gateway, conversation service, model inference, data stores), and finally discuss key trade-offs like latency vs. cost, consistency vs. availability, and model selection. Emphasize scalability, reliability, and user experience throughout.
Pro tip: Show awareness of OpenAI's specific challenges: handling long conversations with context windows, managing rate limits and abuse, and optimizing GPU utilization. Mention concrete numbers (e.g., p99 latency targets) to demonstrate practical experience.
Ask questions to understand scope: expected QPS, latency targets, conversation length, multi-turn context, user authentication, moderation, and cost constraints. Distinguish must-haves from nice-to-haves.
Sketch components: load balancer, API gateway (auth, rate limiting), conversation service (session management, context assembly), model inference service (with GPU pool), vector database for long-term memory, and data stores for user profiles and logs.
Pick 1-2 components to detail, e.g., how to manage conversation state and context window, or how to scale model inference with batching and caching. Discuss data models and API contracts.
Discuss trade-offs: latency vs. cost (model size, caching), consistency vs. availability (session state), and build vs. buy (using existing LLM APIs vs. self-hosted). Justify choices based on requirements.
Summarize how the design scales (horizontal scaling, sharding), handles failures (retries, fallbacks), and monitors performance (metrics, logging, tracing). Mention future improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about chunked HTTP responses and server-sent events.
Start by clarifying the requirements: define 'low first-token latency' (e.g., <100ms) and understand the expected load and model characteristics. Then propose a system design that optimizes the critical path from request to first token, using techniques like connection pre-warming, prompt caching, and streaming protocols. Finally, discuss trade-offs and how you would measure and monitor latency.
Pro tip: Emphasize that first-token latency is dominated by network and preprocessing, not just model inference; show you understand the full pipeline and can optimize each stage. Also, mention that you would set up a latency budget and use distributed tracing to identify bottlenecks.
Define what 'low first-token latency' means quantitatively (e.g., p99 < 200ms) and understand the expected traffic patterns, model size, and hardware constraints.
Identify and minimize latency in each stage: network (use HTTP/2 or WebSockets, keep connections alive), request parsing, prompt processing (cache common prefixes), and model inference (use optimized kernels, speculative decoding).
Implement token streaming using server-sent events (SSE) or WebSockets, ensuring that the first token is sent as soon as it's available, and subsequent tokens are streamed incrementally.
Instrument the system to measure first-token latency at various percentiles, set up alerts, and use distributed tracing to pinpoint regressions.
Acknowledge trade-offs such as cost (pre-warming, caching), complexity (managing stateful connections), and potential impacts on throughput or model quality.
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Start by clarifying requirements such as conversation length, latency, cost, and accuracy needs. Then propose a layered strategy: use summarization and retrieval to compress and selectively include history, and implement a sliding window with a summary buffer. Finally, discuss trade-offs and how to evaluate the approach.
Pro tip: Emphasize that context management is not just about truncation but about preserving semantic continuity; mention that you would measure the impact on model performance and user experience through A/B tests and metrics like task success rate.
Ask about expected conversation length, latency and cost budgets, and the importance of long-term memory. This ensures your solution aligns with business and technical constraints.
Combine summarization of older turns with a sliding window of recent turns, and use retrieval to fetch relevant past information when needed. This balances recency and long-term context.
Use an LLM to periodically summarize the conversation, and store summaries and key entities in a vector database for retrieval. Retrieve only the most relevant pieces to fit within the context limit.
When the context limit is reached, prioritize recent and high-importance information, and drop or compress less relevant parts. Ensure the model still has access to critical facts.
Measure performance with metrics like response quality, task completion, and latency. Use A/B testing to compare strategies and refine the approach based on real user data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (model size, latency SLOs, traffic patterns) and then walk through the stack layer by layer: request routing, batching, GPU scheduling, and autoscaling. Emphasize trade-offs between latency and throughput, and how autoscaling policies must account for GPU warm-up times and cost.
Pro tip: Mention that GPU autoscaling isn't just about adding replicas—it's about managing cold starts and pre-warming instances, and you can use predictive scaling based on historical traffic patterns to stay ahead of demand.
Ask about model size, latency SLOs, expected QPS, cost budget, and hardware availability. This shapes all subsequent design decisions.
Outline components: load balancer, request queue, dynamic batching, model server (e.g., Triton, TensorRT), and GPU workers. Explain how requests flow and where optimizations occur.
Discuss metrics (GPU utilization, queue length, latency), scaling policies (horizontal/vertical), and the need for predictive scaling due to slow GPU provisioning.
Cover latency vs. throughput, cost vs. performance, and strategies for graceful degradation (e.g., fallback to smaller models, request shedding).
Recap the design, highlight monitoring and observability, and suggest iterative improvements based on real-world feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket at the API gateway layer, with per-user and per-org limits stored in Redis.
Start by clarifying requirements: what dimensions to limit (requests, tokens, cost), per-user vs global, and acceptable latency. Then propose a distributed architecture using a fast in-memory store like Redis with atomic operations, and discuss trade-offs between accuracy and performance. Finally, cover quota enforcement, monitoring, and graceful degradation.
Pro tip: Mention that rate limiting should be applied at multiple layers (edge, service, and per-model) and that you'd use a sliding window or token bucket algorithm with Redis Lua scripts for atomicity. Also highlight the importance of returning informative headers like X-RateLimit-Remaining and Retry-After to help clients back off gracefully.
Ask about the scale (requests per second, number of users), what to limit (requests, tokens, compute cost), and whether limits are per-user, per-API-key, or global. Also consider latency and consistency requirements.
Discuss algorithms like fixed window, sliding window, token bucket, or leaky bucket, and justify your choice based on burst tolerance and accuracy. For high concurrency, a sliding window with Redis sorted sets or a token bucket with Lua scripting is common.
Propose using a centralized in-memory data store like Redis for shared state, with atomic operations (Lua scripts) to avoid race conditions. Consider sharding or local caching to reduce latency, and discuss how to handle Redis failures (e.g., fallback to local limits).
Explain how to track usage over longer periods (e.g., daily/monthly quotas) using counters with TTL, and how to enforce them. Include monitoring and alerting for limit breaches, and a way to dynamically adjust limits.
Discuss returning proper HTTP status codes (429 Too Many Requests) and headers (X-RateLimit-Limit, X-RateLimit-Remaining, Retry-After). Also cover idempotency, retries with exponential backoff, and how to handle distributed denial-of-service (DDoS) scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about running a lightweight classifier before the main model call to catch obvious violations, then a post-processing filter on outputs.
Start by clarifying requirements and constraints, then propose a multi-layered pipeline that covers both inputs and outputs, emphasizing trade-offs between safety, latency, and user experience. Structure your answer around detection, classification, action, and feedback loops, and discuss how to evaluate and iterate on the system.
Pro tip: Show awareness that safety is an adversarial problem: attackers constantly evolve, so the pipeline must include continuous monitoring, red-teaming, and rapid iteration. Also, highlight the importance of measuring false positives/negatives and their impact on user trust.
Ask about scale, latency requirements, types of harmful content, regulatory constraints, and acceptable trade-offs between safety and user experience.
Propose a layered approach: fast, cheap filters (e.g., regex, blocklists) first, then ML classifiers for nuanced detection, and finally human review for edge cases.
Specify what happens when harmful content is detected: block, flag, sanitize, or escalate. Consider user feedback and appeals.
Set up logging, metrics, and dashboards to track performance. Use adversarial testing and user reports to continuously improve models.
Define evaluation metrics (precision, recall, latency, user impact) and establish a process for regular model updates and policy adjustments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through the usual: distributed tracing, per-request token counts, queue depth metrics, GPU utilization dashboards.
Start by outlining a layered observability strategy covering metrics, logs, traces, and profiling, tailored to the system's architecture. Then walk through a systematic debugging process for latency spikes, moving from detection to root cause using data from each layer.
Pro tip: Emphasize that observability should be designed proactively, not bolted on after an incident, and that debugging should follow a hypothesis-driven approach using high-cardinality data to avoid guesswork.
Describe the key observability components: metrics (latency, error rates, throughput), logs (structured, with context), traces (distributed tracing), and continuous profiling. Explain how each helps understand system behavior.
Detail specific metrics to track: p50/p95/p99 latency, error rates, saturation (CPU, memory, I/O), and queue depths. Mention using OpenTelemetry for standardized instrumentation and correlation across signals.
Explain how to set up alerting based on SLOs and anomaly detection to catch latency spikes early. Include dashboards for real-time monitoring and historical analysis.
Walk through a step-by-step debugging process: confirm the spike, scope impact (which services/regions), correlate with recent changes, and drill down using traces to identify the bottleneck (e.g., slow DB query, network issue, resource contention).
Describe immediate mitigation (e.g., rollback, scale up) and long-term fixes (e.g., optimize code, add caching). Emphasize post-mortem and improving observability based on gaps found.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.