Embeddings System Design Interview Questions

Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.

Hard RAG interview question on rag for structured + unstructured data — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on late chunking and contextual retrieval — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on graphrag vs vector rag — architecture, trade-offs, eval, and production patterns.

Multi-agent orchestration is a senior/staff-level system design question gaining traction at Google DeepMind and Microsoft. Learn the supervisor pattern, shared state management, and how to avoid the 'too many agents' anti-pattern that sinks most candidate answers.

This is an enterprise platform design question spanning agents, MCP, governance, and observability.

Hard vector DB question on tenant isolation, noisy neighbors, and ACL safety.

**Graph structure** draft → auto_checks → legal_subgraph → manager_subgraph → publish; reject edges loop to draft with comments in state.

**State schema** ticket_id, customer_tier, messages, retrieved_docs, proposed_actions, sentiment, escalation_reason, resolution_code.

Hard AI Agents interview question on customer support agent system design — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on agent memory: short-term, long-term, episodic — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on cost of embeddings at 100m documents — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on real-time rag with fresh data — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on pinecone vs weaviate vs pgvector vs opensearch — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on embedding model selection and migration — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on conversational rag with chat history — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on multilingual rag challenges — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on cross-encoder vs bi-encoder reranking — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on hybrid search: bm25 + dense vectors explained — architecture, trade-offs, eval, and production patterns.

Scenario question on cross-model tokenizer bugs — chunk boundaries, context overflow, and embedder vs LLM alignment.

Hard scenario on agent memory — episodic vs semantic memory, vector stores, summarization, and model parametric limits.

Hard scenario on PII in LLM systems — detection, redaction, DPA compliance, log minimization, and regional deployment.

Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.

Scenario question on LLM latency — prefill vs decode, retrieval overhead, streaming, caching, and model routing fixes.