← Anthropic Interview Insights
Start by clarifying requirements and scope with the interviewer, then outline a high-level architecture covering the frontend, backend, and data storage. Dive into key components like streaming, versioning, and API call generation, discussing trade-offs and scalability. Conclude by summarizing how the design meets the needs of developers.
Pro tip: Emphasize the importance of a great developer experience: fast iteration, clear error messages, and seamless integration with existing workflows. Show awareness of Anthropic's focus on safety and reliability by mentioning how you'd handle rate limiting and sensitive data.
Ask questions to understand the target users, expected scale, and must-have features. Confirm the core functionalities: prompt editing, parameter tuning, streaming generations, versioning, and API call copying.
Sketch the main components: a web frontend (React), a backend API (Node.js/Python), a database for prompts and versions, and integration with LLM providers. Discuss how streaming will be implemented (e.g., WebSockets or Server-Sent Events).
Design schemas for prompts, versions, and user data. Explain how to handle versioning (e.g., immutable versions with a pointer to latest) and how to efficiently store and retrieve prompt history.
Detail the streaming mechanism: how the backend calls the LLM API, receives token streams, and forwards them to the frontend. Discuss error handling, reconnection, and performance considerations.
Explain how to generate the equivalent API call (e.g., cURL, Python) based on the prompt and parameters. Ensure it's accurate and includes authentication placeholders. Discuss UI for easy copying.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with Server-Sent Events pretty quickly and explained why: unidirectional, proxy-friendly, browser handles reconnect automatically.
Start by framing the problem as a real-time streaming challenge, then propose a transport mechanism (e.g., SSE or WebSockets) with clear trade-offs. Walk through the end-to-end request path, highlighting how each component handles streaming and backpressure.
Pro tip: Mention that you'd use SSE for unidirectional streaming from server to client, but if the client needs to send mid-stream messages (e.g., for tool use or interruptions), WebSockets are more appropriate. Also, discuss how to handle connection drops and resume streams with event IDs.
Ask about expected latency, throughput, client types (browser, mobile), and whether bidirectional communication is needed. This determines the transport choice.
Compare SSE, WebSockets, and HTTP/2 streaming. Justify your choice based on requirements, e.g., SSE for simplicity and unidirectional flow, WebSockets for full-duplex.
Outline the flow: browser initiates request -> load balancer -> API gateway -> backend service -> model inference service. Explain how each hop supports streaming (e.g., HTTP/1.1 chunked encoding, HTTP/2).
Describe how to manage slow clients, buffer limits, and retries. Mention techniques like flow control, timeouts, and graceful degradation.
Explain how to scale horizontally (e.g., stateless services, sticky sessions if needed) and what metrics to track (latency, error rates, active connections).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My instinct was to throw more web servers at it, which is wrong.
Start by clarifying the scale and constraints, then focus on the GPU inference bottleneck as the critical resource. Propose a multi-layered architecture that optimizes GPU utilization, manages long-lived connections efficiently, and scales horizontally with load balancing and autoscaling.
Pro tip: Emphasize that scaling is not just about adding GPUs; it's about maximizing throughput per GPU through techniques like continuous batching, quantization, and speculative decoding, while ensuring graceful degradation under load.
Ask about expected QPS, latency SLAs, model size, and whether the streaming is bidirectional. Understand the cost and availability constraints for GPUs.
Discuss techniques to increase GPU throughput: continuous batching, model quantization, speculative decoding, and using specialized hardware like TPUs. Consider model parallelism for large models.
Handle millions of long-lived connections with efficient load balancing (e.g., L4/L7), connection multiplexing, and using protocols like WebSockets or gRPC streaming. Ensure sticky sessions if needed.
Deploy inference servers in a cluster with autoscaling based on GPU utilization and queue depth. Use a message queue to decouple request ingestion from GPU processing.
Implement rate limiting, request prioritization, and fallback mechanisms (e.g., smaller models) when GPU capacity is exhausted. Monitor and alert on key metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what does 'side-by-side' mean (UI layout, data alignment), what metrics matter (latency, cost, output quality), and what scale (number of variations, concurrent users). Then propose a high-level architecture that streams both prompts concurrently, collects outputs and cost data in real-time, and presents them in a synchronized view. Finally, discuss trade-offs around concurrency, cost calculation, and user experience.
Pro tip: Emphasize the importance of a unified streaming protocol and cost tracking per token to ensure accurate, real-time comparison. Also, mention that you'd design for extensibility to support more than two variations and different model providers.
Ask questions to understand the exact needs: Is this for internal testing or customer-facing? What metrics are critical (latency, cost, quality)? What's the expected scale? This ensures the design meets the actual use case.
Propose using asynchronous requests (e.g., asyncio, goroutines) to send both prompts simultaneously to the model API. Use a unified streaming interface that yields chunks from both streams, tagging each chunk with its source variation.
Calculate cost incrementally as tokens are received, using the model's pricing per token. Maintain separate counters for each variation and update the UI in real-time. Consider caching or batching for efficiency.
Use a frontend framework that supports reactive updates (e.g., React, Vue) to display both outputs and cost metrics side-by-side. Ensure that as chunks arrive, they are appended to the correct column and cost values update live.
Address trade-offs: concurrency limits, error handling (one stream fails), cost calculation accuracy (streaming vs. final), and UI performance. Suggest how to extend to N variations or different providers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Temperature zero runs are deterministic so you can cache those.
Start by framing caching as a trade-off between latency/cost and freshness/accuracy, then walk through the generative AI pipeline (input, model inference, output) to identify where caching helps and where it fails. Use concrete examples like prompt caching for repeated system prompts, semantic caching for similar queries, and KV cache for autoregressive decoding, while highlighting breakdowns due to non-determinism, personalization, and context sensitivity.
Pro tip: Emphasize that caching in generative AI is not just about speed—it's about managing the cost and latency of expensive model calls, but you must carefully handle cache invalidation and staleness because model outputs can vary with context and sampling parameters.
Break down the product into stages: input processing, model inference (prefill and decode), and output post-processing. Identify where repeated computations occur.
For each stage, list where caching can help: e.g., caching system prompts, few-shot examples, embeddings, KV cache for decoding, and semantic caches for similar queries.
Discuss where caching fails: non-deterministic outputs, personalized or context-dependent responses, rapidly changing data, and cache invalidation complexity.
For each breakdown, propose mitigations like TTLs, cache key design, fallback to fresh inference, or hybrid approaches. Weigh latency/cost savings against accuracy risks.
Summarize which caching strategies are most impactful for the given product, considering user experience, cost, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-user concurrency caps enforced at the gateway before requests hit the queue.
Start by clarifying the requirements: what defines a 'user', what are the fairness goals, and what are the latency and throughput constraints. Then propose a multi-layered solution that combines admission control, per-user concurrency limits, and a fair scheduling algorithm, while discussing trade-offs between strictness and user experience.
Pro tip: Emphasize that fairness is not just about limiting concurrency but also about ensuring that long-running requests don't starve short ones; mention techniques like weighted fair queuing or deficit round-robin to achieve this.
Ask about the definition of a user, expected traffic patterns, latency SLOs, and whether quotas should be hard or soft. This ensures the solution aligns with business needs.
Propose a token bucket or leaky bucket per user to limit request rate, and a semaphore or counter to cap concurrent long-running generations per user.
Use a scheduling algorithm like weighted fair queuing or deficit round-robin to allocate inference capacity proportionally, preventing any single user from monopolizing resources.
Suggest monitoring per-user usage and system load, with the ability to adjust quotas dynamically based on demand or user tiers.
Address trade-offs between fairness and utilization, potential starvation, and how to handle edge cases like bursty traffic or abusive users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by describing the client experience: the user sees a partial stream (600 tokens) and then an error or hang, with no way to continue. Then propose a resumable streaming design using a server-side generation ID and token offset, so the client can reconnect and fetch remaining tokens without re-running the model. Emphasize trade-offs like state management, idempotency, and cost savings.
Pro tip: Mention that you should persist the generation state (e.g., in Redis) with a TTL and use a cursor-based resume, but also consider client-side buffering and retry logic to handle transient network issues gracefully.
Explain that the client receives 600 tokens, then the connection drops, causing an incomplete response and likely an error. The user sees a partial output and may need to retry, losing progress.
Re-running the whole generation wastes compute, increases latency, and may produce different output due to non-determinism. The goal is to resume from token 600 without re-generating earlier tokens.
Use a server-side generation session with a unique ID. The server buffers or persists generated tokens (e.g., in Redis) with a TTL. The client reconnects with the generation ID and last received token index, and the server streams from that point.
Discuss state management overhead, idempotency, TTL for cleanup, and handling multiple reconnects. Consider client-side buffering and exponential backoff for retries.
Highlight cost savings, improved UX, and reliability. Mention alternatives like client-side retry with full regeneration if stateful resume is too complex.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.