This was a lot more surface area than I budgeted time for.
Start by clarifying requirements and constraints, then outline a modular architecture that separates concerns like API client, retry logic, rate limiting, batching, and logging. Discuss trade-offs for each component and how they integrate, emphasizing configurability, scalability, and observability.
Pro tip: Mention that you would use exponential backoff with jitter and respect the Retry-After header to avoid thundering herd problems, and that you'd implement rate limiting with a token bucket algorithm to smooth out bursts.
Ask about expected throughput, latency requirements, error handling expectations, and whether the library needs to support multiple LLM providers. This ensures the design meets actual needs.
Outline modules: a configurable client for API calls, a retry handler with backoff, a rate limiter, a batcher, and a logger. Explain how they interact and the interfaces between them.
Detail implementation strategies for retries (exponential backoff with jitter), rate limiting (token bucket), batching (grouping prompts by size/time), async/streaming (using async generators), and structured logging (JSON logs with context).
Compare approaches for each feature, such as fixed vs exponential backoff, client-side vs server-side rate limiting, and how to handle partial failures in batching. Mention idempotency and error propagation.
Recap the design, highlight how it meets requirements, and suggest potential extensions like caching, circuit breakers, or metrics integration for production readiness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pushed pretty hard on the integration story.
Start by framing your API design around the core use cases and constraints of the library, then walk through key decisions like abstraction level, error handling, and extensibility. Finally, explain how you would integrate it into a production system, covering dependency management, configuration, observability, and scaling considerations.
Pro tip: Show that you think about API design as a product: consider developer experience, versioning, and backward compatibility from day one. Also, mention how you'd gather feedback and iterate on the API based on real-world usage.
Briefly restate the library's purpose and the key constraints (e.g., performance, ease of use, target users) that shaped your API design.
Discuss decisions like function signatures, data models, error handling, and extensibility points, and justify them with trade-offs.
Describe how the library would be consumed: dependency injection, configuration, logging, monitoring, and testing within a production environment.
Cover aspects like performance, scalability, security, versioning, and how you'd handle breaking changes or deprecations.
Wrap up by reiterating the most important design principles and how they enable smooth integration and long-term maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by categorizing the failure modes you encountered (e.g., transient, permanent, partial) and explain how you mapped each to a specific handling strategy. Then discuss the tradeoffs you made between reliability, complexity, latency, and cost, using concrete examples to illustrate your reasoning.
Pro tip: Quantify the impact of your tradeoffs—e.g., 'We accepted a 0.1% increase in error rate to reduce p99 latency by 200ms'—to show you think in terms of measurable outcomes. Also, mention how you validated your error handling through chaos testing or fault injection.
Identify and classify the different types of failures you anticipated or encountered, such as transient network errors, permanent validation errors, or partial system outages.
For each failure category, describe the specific error handling techniques you applied, such as retries with exponential backoff, circuit breakers, fallbacks, or fail-fast validation.
Discuss the tradeoffs involved in your choices, such as increased complexity vs. improved resilience, added latency vs. consistency, or cost vs. reliability.
Provide a specific instance where your error handling made a difference, including the problem, your solution, and the measurable outcome.
Summarize what you would do differently or how you iterated on your approach based on monitoring, incidents, or feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.