OpenAI RAG Engineer Interview Questions

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

Hard RAG pattern question on corrective retrieval and gated web fallbacks.

Hard RAG ops question on index versioning, dual-write, and blue-green cutovers.

Hard RAG interview question on openai interview: customer support rag — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on evaluating rag: faithfulness, context precision, recall — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on chunking strategies for different document types — architecture, trade-offs, eval, and production patterns.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Medium GPT API question on structured outputs reliability vs JSON mode.

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

**Storage** Vector + structured facts table keyed by user_id; version memories on conflict.

**Decision** Try prompt + tools + RAG first; fine-tune if style/reliability gap persists and data exists.

**Model selection** Benchmark retrieval on your corpus — smaller models cheaper if recall OK.

Shared embedding spaces, separate indexes, CLIP-style models, metadata routing, and fusion strategies.

PCA, Matryoshka embeddings, learned compression, and recall impact when shrinking vector size.

Embedding similarity, k-NN example banks, MMR diversity, and eval-driven example curation for few-shot prompts.

Evaluate persona and role prompts for tone, domain framing, and when they add noise without improving task accuracy.

Structured prompting patterns using tags and boundaries to separate instructions, context, tools, and user input in production LLM apps.

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

Hard project question on LLM red-teaming — adversarial prompts, automation, severity rubrics, and fix-verify cycles.

Hard OpenAI company question — chat product system design with safety stack, eval gates, streaming, and grounding.

Hard project question on use-case benchmarking — golden sets, task metrics, regression gates, and leaderboard limitations.

Project question on prefix/prompt caching and KV cache reuse — TTFT optimization and provider billing implications.