Senior
Design a production RAG chatbot
Walk through document ingestion, chunking, embedding, hybrid retrieval, reranking, answer generation, caching, and evaluation. Discuss tradeoffs and failure modes.
RAGVector DBHybrid RetrievalCaching
Interview prep
Get ready for AI engineering interviews — system design, behavioral, and beyond.
AI engineering interviews differ from standard SWE interviews. You need to design RAG pipelines, reason through agent architectures, define evaluation strategies, and articulate production tradeoffs — not just implement data structures.
Key interview tips for AI engineers
Start every system design with: goals, users, scale, and SLAs — before drawing boxes.
For RAG, always mention chunking strategy choice, retrieval recall vs. precision tradeoff, and evaluation.
Name your failure modes: what breaks first, at what scale, and how would you detect it.
Quantify your behavioral answers: latency numbers, reduction percentages, dataset size.
Ask clarifying questions out loud — interviewers reward explicit reasoning.
Mention observability in every design: what would you monitor and how would you alert?
System design questions
Senior
Design an AI customer support agent
Cover tool selection, agent loop design, memory architecture, escalation logic, guardrails, audit trail, and human approval workflows.
AgentsToolsMemoryGuardrails
Staff
Design an LLM evaluation platform
Discuss dataset management, evaluator types (deterministic, LLM-as-judge, human), regression test runner, CI integration, and result dashboards.
LLMOpsEvaluationCI/CD
Staff
Design an LLM gateway and router
Architect a multi-model gateway with routing logic, rate limiting, cost attribution, fallback chains, prompt versioning, and observability.
LLM GatewayRoutingObservability
Senior
Design a vector database for production
Cover indexing strategies (HNSW, IVF), hybrid search, metadata filtering, multi-tenancy, consistency tradeoffs, and scaling from 1M to 1B vectors.
Vector DBHNSWMulti-tenantScale
Senior
Design an AI observability stack
Walk through trace collection, cost aggregation, latency monitoring, quality metrics, dashboard design, alerting, and incident response playbooks.
ObservabilityTracingAlerting
Full mock interview answer bank
50+ questions with model answers, diagrams, and evaluation criteria — unlocked with premium.
Behavioral questions
Use the STAR format: Situation, Task, Action, Result. Every answer should include a quantified result related to AI system quality, cost, or reliability.
STAR
Tell me about a time you reduced hallucinations in an LLM system.
Describe how you diagnosed the hallucination root cause
Explain the retrieval, prompt, or evaluation changes you made
Quantify the improvement using faithfulness or accuracy metrics
STAR
Describe a production incident caused by an AI system and how you handled it.
Be specific about the failure mode (hallucination, retrieval failure, cost spike)
Walk through your triage and rollback process
Explain what monitoring or tests you added afterwards
STAR
How did you evaluate the quality of an LLM feature before shipping it?
Describe the evaluation metrics you chose and why
Explain how you built or sourced your evaluation dataset
Discuss how you handled disagreement between automated and human evaluators
STAR
Tell me about a time you had to make a tradeoff between AI quality and cost or latency.
Set up the business constraint that forced the tradeoff
Explain what you measured and how you made the decision
Discuss how you validated the chosen tradeoff in production
Recommended preparation resources
Practice quizzes
Test your knowledge on RAG, agents, LLMOps, system design, evaluation, and more.
Open quizzesLesson library
Read production-focused lessons with architecture diagrams, failure modes, and interview angles.
Browse lessonsSystem Design track
The AI System Design Interview track covers 11 topics with model answers and behavioral prep.
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