5 exercises — choose the best-structured answer to common Full-Stack AI Engineer interview questions. Focus on precise vocabulary, correct use of technical terms, and demonstrating real experience.
Structure for Full-Stack AI Engineer answers
Tip 1: Connect frontend to backend: SSE/WebSockets for streaming, optimistic UI for async LLM calls
Tip 3: Evaluation: LLM-as-judge, RAGAS metrics, golden dataset regression tests
Tip 4: Cost control: prompt caching, token budgets, model routing by task complexity
0 / 5 completed
1 / 5
The interviewer asks: "How do you stream LLM responses to the frontend?" Which answer best demonstrates full-stack integration knowledge?
Option B is strongest because it names the correct protocol (SSE), explains the data flow end-to-end, and addresses practical concerns like error handling and UI layout. Key structure: backend streams → SSE chunks → EventSource/ReadableStream → progressive UI append. Option A eliminates streaming entirely. Option C is not wrong but SSE is simpler for unidirectional (server → client) streaming. Option D (polling) adds unnecessary latency and server load.
2 / 5
The interviewer asks: "Walk me through the architecture of a production RAG pipeline." Which answer best demonstrates end-to-end RAG knowledge?
Option B is strongest because it names all five pipeline stages with specific tooling at each stage, including the often-omitted reranking step. Key structure: ingest → retrieve → rerank → augment → evaluate. Option A describes a minimal proof-of-concept, not a production pipeline. Option C names a framework but does not demonstrate architectural understanding. Option D (fine-tuning) is a different paradigm — it does not handle knowledge that changes after training.
3 / 5
The interviewer asks: "How do you evaluate the quality of an LLM-powered feature?" Which answer best demonstrates a multi-layer evaluation strategy?
Option C is strongest because it covers all evaluation layers: offline regression tests, automated LLM judging, domain-specific RAG metrics, and online user signals. Key structure: golden dataset → LLM-as-judge → RAGAS → A/B testing → online monitoring. Option A (informal feedback) does not scale and is not reproducible. Option B uses exact match, which fails for generative outputs. Option D conflates token efficiency with quality — unrelated concepts.
4 / 5
The interviewer asks: "How do you handle prompt injection attacks in a user-facing AI feature?" Which answer best demonstrates security-aware engineering?
Option B is strongest because it describes defence-in-depth across multiple layers rather than relying on any single control. Key structure: role separation → input validation → output schema → privilege limits → monitoring. Option A relies on the LLM following its own instructions — this is exactly what prompt injection bypasses. Option C only catches offensive content, not instruction override attacks. Option D removes the system prompt entirely, eliminating the primary safety control.
5 / 5
The interviewer asks: "How do you reduce the cost of LLM API calls in a high-traffic application?" Which answer best demonstrates cost-optimisation expertise?
Option B is strongest because it identifies five complementary cost levers, each targeting a different cost driver. Key structure: prefix caching → model routing → semantic caching → batching → token reduction. Option A (session cache) only helps repeat users with the same query. Option C trades API cost for infrastructure cost and does not address the optimisation problem. Option D reduces one small input cost while ignoring the larger drivers.