I went with retry/fallback logic because it felt most concrete and I could show real decision-making fast.
Pick one component (e.g., retry/fallback) and walk through a concrete design, explicitly stating assumptions and trade-offs. Use the AI assistant as a tool to accelerate coding, but focus your answer on architecture, failure modes, and how you validated the AI-generated code.
Pro tip: Treat the AI assistant as a junior engineer: review its output, test edge cases, and be ready to explain why you accepted or rejected its suggestions. This shows you can leverage AI without blindly trusting it.
Ask clarifying questions about expected traffic, provider SLAs, budget constraints, and whether the component must be stateless or can use shared state. Define what 'success' looks like for the chosen component.
Select one of the three options and sketch a high-level architecture: interfaces, data flow, and key modules. Mention how the AI assistant will be used (e.g., to generate boilerplate, suggest algorithms, or write tests).
Explain the core logic (e.g., retry with exponential backoff, fallback provider selection, token counting and enforcement). Discuss trade-offs like latency vs. reliability, cost vs. performance, and simplicity vs. flexibility.
Describe how the component handles provider outages, rate limits, malformed responses, and partial failures. Include monitoring, logging, and alerting considerations.
Explain how you used the AI assistant to generate tests, simulate failures, and refactor code. Highlight any corrections or improvements you made to the AI's output.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then present a layered architecture: API gateway, provider abstraction, rate limiting, caching, streaming, observability, and auth/quota. Walk through each component, highlighting trade-offs and how the design accommodates new providers with minimal changes.
Pro tip: Emphasize idempotency and graceful degradation: when a provider fails, fallback to another or return cached responses, and ensure streaming connections handle backpressure and timeouts. Also, discuss how you'd measure and monitor provider-specific latency and error rates to inform routing decisions.
Ask about expected QPS, number of tenants, models, providers, latency SLOs, and compliance needs. This shapes decisions on rate limiting granularity, caching strategy, and observability depth.
Outline the main components: API gateway for auth and routing, provider abstraction layer for unified interface, rate limiter, cache, streaming handler, and observability pipeline. Explain how requests flow through these.
For each subsystem, describe implementation choices: e.g., token bucket for rate limiting, Redis for caching, SSE/WebSockets for streaming, OpenTelemetry for tracing. Discuss trade-offs like consistency vs. latency.
Explain how to add a new provider: implement adapter, register in provider registry, update routing rules, and ensure observability and rate limiting work out of the box. Mention configuration-driven approach.
Recap the design, highlighting how it meets requirements. Discuss potential bottlenecks, failure modes, and future improvements like intelligent routing based on cost/latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.