5 exercises — choose the best-structured answer to common LLM Application Engineer interview questions. Focus on RAG design, evaluation, prompt engineering, reliability, and cost optimisation.
Structure for LLM Application Engineer interview answers
Explain the architecture, not just the tool: describe RAG retrieval pipeline stages, chunking strategy, and reranking
Quantify evaluation: name specific metrics (faithfulness, context recall, answer relevancy) and explain what they measure
Cover failure modes: LLM systems fail in specific ways — hallucination, context overflow, latency — address each
Show cost awareness: token cost, latency, and quality form a triangle — demonstrate you can navigate trade-offs
0 / 5 completed
1 / 5
The interviewer asks: "Design a RAG system for a technical support chatbot that answers questions from a 10,000-page documentation corpus. What are the key architectural decisions?" Which answer best covers the full design?
Option B covers six architectural layers with specific decisions and trade-offs at each: semantic chunking strategy (vs fixed-size), embedding model evaluation, metadata-filtered vector stores, two-stage retrieval with reranker, generation prompt design (citation + "I don't know" instruction), and evaluation metrics. The reranker stage and HyDE mention are senior-level details. Option A describes the basic pattern but misses chunking strategy, reranking, metadata filtering, and evaluation. Options C and D are too high-level for an architecture question.
2 / 5
The interviewer asks: "How do you evaluate an RAG system in production, and what metrics do you track?" Which answer best covers the evaluation framework?
Option B covers seven specific metrics across two layers (component: context recall, precision, faithfulness, answer relevancy; end-to-end: correctness, latency, cost), explains what each metric measures and what low scores indicate, mentions RAGAS as an implementation framework, and adds production monitoring practices (sampling + drift alerting). Options A and D rely on user feedback only — a lagging indicator that cannot isolate failures in the retrieval vs generation components. Option C names RAGAS but does not demonstrate understanding of what it measures.
3 / 5
The interviewer asks: "What techniques do you use to reduce hallucination in LLM-based applications?" Which answer demonstrates the most comprehensive approach?
Option B provides eight distinct techniques across multiple layers: architectural (RAG, constrained schemas), generation-time (chain-of-thought + citations, temperature, consistency), post-generation (faithfulness verification, RAVen), and context management (preventing overflow). The breadth and specificity demonstrate production LLM engineering experience. Option C identifies RAG correctly but treats it as the only technique. Options A and D each identify one technique without the system-level perspective.
4 / 5
The interviewer asks: "How do you manage LLM API costs at scale without sacrificing quality?" Which answer demonstrates the best cost optimisation strategy?
Option B provides seven specific cost optimisation techniques in priority order, with concrete numbers (40-60% savings from routing, 20-40% cache hit rates, 30-50% prompt compression, 50% batch discount), and includes the fine-tuning path for high-volume tasks. It also covers cost monitoring as a security signal (prompt injection detection). Option A identifies two correct techniques but with no implementation detail. Option C gives a blanket model downgrade recommendation without segmentation logic. Option D mixes cost and latency techniques without depth.
5 / 5
The interviewer asks: "How do you design an LLM system for reliability — handling failures, latency variability, and model deprecations?" Which answer best covers production reliability?
Option B provides six reliability mechanisms with concrete thresholds: retry + fallback cascade (primary → secondary → graceful degradation), streaming for timeout resilience, circuit breaker with specific trigger conditions, latency variability handling (streaming + async queue), model deprecation workflow (version pinning, shadow mode), and observability requirements (log schema, alert thresholds). Option A identifies two techniques without architecture. Option C mentions the right concepts but lacks implementation depth. Option D dismisses reliability as a concern — incorrect for production LLM systems where P99 latency is commonly 10-30× P50.