production
Applied AI interview questions tagged production, across every topic.
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Concepts behind "production"
The curriculum that explains the ideas these questions test.
Foundational
The LLM GatewayAn LLM gateway is a single proxy layer between your application and one or more model providers. It centralizes the cross-cutting concerns every LLM app needs: routing and fallback across models/providers, caching, rate limiting, authentication, cost tracking, observability, and guardrails. It also prevents vendor lock-in by abstracting providers behind one interface. Applied-AI interviews probe it because it is the backbone of a production LLM platform and the place most operational controls live.⚙️ System Design for AI in Production
Foundational
Latency Budgets and StreamingLLM latency is not one number: time-to-first-token (set by prefill and queueing) and inter-token latency (set by decode) feel very different to users. Streaming tokens as they generate hides total latency by showing progress immediately. Designing to a latency budget means allocating time across retrieval, model, and tools, measuring TTFT and tokens-per-second (not just end-to-end), and using streaming, caching, and routing to hit it. Applied-AI interviews probe it because perceived latency makes or breaks LLM UX.⚙️ System Design for AI in Production
Foundational
GuardrailsGuardrails are the runtime safety layer wrapping an LLM: input checks (detect prompt injection, off-topic or disallowed requests, PII) before the model, and output checks (content safety, schema/format validation, grounding, PII/secret leakage) before the user. They are built from rules, classifiers, judge models, and validators, with a defined fail-safe action when one trips. Applied-AI interviews probe it because 'add guardrails' is hand-wavy, and the concrete input/output checks plus fail-safe behavior are what make a deployment safe.⚙️ System Design for AI in Production
Foundational
Rate Limiting, Retries, and BackoffLLM systems depend on rate-limited, sometimes-failing providers, so resilient design is essential. Rate limiting (token bucket) protects your service and enforces per-tenant quotas; retries with exponential backoff and jitter handle transient failures without hammering a struggling dependency; circuit breakers stop sending requests to a failing service to let it recover. Applied-AI interviews probe it because LLM calls are slow, expensive, and flaky, and naive retry logic turns a blip into an outage.⚙️ System Design for AI in Production
Foundational
Observability for LLM SystemsYou cannot operate or improve an LLM system you cannot see. Observability means logging every request end to end, inputs, retrieved context, prompt and model version, output, tokens, latency, and cost, plus tracing multi-step agent/RAG flows and tracking quality signals. It is the basis of debugging, cost attribution, evaluation, and incident response. Applied-AI interviews probe it because LLM systems fail silently (a plausible-but-wrong answer throws no error), so visibility is what makes them debuggable and trustworthy.⚙️ System Design for AI in Production
Foundational
LLM Cost OptimizationLLM systems get expensive fast, and the cost model is mostly tokens and number of model calls. The levers, in rough order of impact: route easy queries to cheaper/smaller models, cache repeated and similar requests, trim context (fewer, better chunks), use cheaper retrieval/reranking, and for agents cut unnecessary steps. The discipline is measuring cost per request and attacking the dominant contributor. Applied-AI interviews probe it because cost is a primary production constraint and most teams overspend by defaulting to the biggest model on everything.⚙️ System Design for AI in Production