5 exercises — choose the best-structured answer to common Full-Stack AI Engineer interview questions. Focus on production-grade LLM integration, resilience design, and AI product vocabulary.
Structure for Full-Stack AI integration questions
Security first: never expose API keys; always proxy through backend
Multiple layers: latency, cost, reliability, and safety are all dimensions
Fallback by default: every AI feature needs a non-AI fallback
Name specific tools: Redis, circuit breaker, exponential backoff + jitter
0 / 5 completed
1 / 5
The interviewer asks: "How do you integrate an LLM API into a production web application — what does your architecture look like?" Which answer best demonstrates production-readiness thinking?
Option B is the strongest: it covers the critical security concern (no frontend key exposure), describes the complete architecture with named components, lists specific production concerns (rate limiting per user, output validation, caching, retry logic, circuit breaker, cost tracking). Option C is correct on security but minimal — a senior engineer should also address retry logic, caching, output validation, and cost tracking. Option D is a good partial answer — edge deployment for latency is valid — but misses output validation, cost tracking, and graceful degradation. Option A is a critical security mistake: never expose API keys in frontend code. Key production integration checklist: security → architecture diagram → latency optimization → reliability (retry/circuit breaker) → cost management → output safety.
2 / 5
The interviewer asks: "How do you handle streaming responses from an LLM in your frontend?" Choose the most technically complete answer.
Option B is the strongest: it covers both server-side (SSE/chunked transfer) and client-side implementation (ReadableStream, Uint8Array decoding, SSE parsing, progressive state updates), adds UX considerations (typing indicator), error handling (error boundaries), and user control (abort controller). Option A is correct but too brief for a senior role. Option C (WebSockets) is technically possible but SSE is preferred for streaming text — WebSockets add bidirectional complexity you don't need for one-way streaming. Option D is partially wrong: "await the full response" defeats the purpose and delays perceived performance by seconds for long responses. Key streaming vocabulary: ReadableStream, getReader(), Uint8Array, SSE format, chunked transfer, abort controller, progressive hydration.
3 / 5
The interviewer asks: "What caching strategies do you use for LLM responses?" Which answer shows the most nuanced understanding?
Option B is the strongest: it identifies three distinct caching layers with specific use cases, technologies (Redis, vector DB), parameters (cosine similarity threshold), and a clear decision framework for when to apply each layer. Option C describes semantic caching correctly but misses the other layers. Option D correctly raises freshness concerns but is too conservative — a blanket short TTL doesn't exploit provider-side KV caching and misses the semantic caching opportunity for open-ended queries. Option A is the most naive implementation. Key vocabulary to know: exact-match cache, semantic cache, cosine similarity threshold, KV cache (provider-side prefix caching), TTL, vector store as cache.
4 / 5
The interviewer asks: "Describe your approach to prompt versioning and A/B testing prompts in production." Choose the most structured answer.
Option B is the strongest: it covers prompt-as-code philosophy, specific tooling options, the exact A/B testing mechanism (user ID hash for deterministic assignment), lists concrete metrics with their types, mentions feature flags for rollback, and includes the statistical rigor requirement (p-value, minimum duration). It also names a common pitfall (multiple comparisons). Option C is solid — version control, feature flags, metrics, 7 days — but lacks statistical rigor and the prompt management tooling layer. Option D is correct about versioning and logging but doesn't cover A/B testing. Option A is too vague ("10% of users"). Advanced vocabulary: deterministic assignment, LLM-as-judge, task completion rate, feature flag rollback, statistical significance, multiple comparison correction.
5 / 5
The interviewer asks: "How do you handle LLM failures and degrade gracefully in a user-facing feature?" Which answer demonstrates the most production-resilient design?
Option B is the strongest: it names four distinct failure types, gives a different handling strategy per failure type (not retrying content filters is an important detail), defines the circuit breaker pattern, mandates non-AI fallbacks for every feature, sets concrete timeout numbers, and specifies the UX treatment. Option C describes basic retry logic with a fixed delay — no jitter, no circuit breaker, no fallback, no timeout budget. Fixed delays can amplify thundering herds during an outage. Option D (async queue) is valid for background AI features but doesn't work for real-time user-facing features where the user is waiting for a response. Option A is the minimum viable handling — not acceptable for a production system. Key pattern language: exponential backoff + jitter, circuit breaker, graceful degradation, fallback hierarchy, timeout budget, thundering herd.