Google AI 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.

Medium RAG interview on hard negatives for embedding/reranker quality.

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 Gemini/Vertex question on context caching vs RAG trade-offs.

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

**Flow** Multimodal intake → diagnose → suggest fix → verify resolved.

**Mechanism** API retrieves search results, model grounds answer with sources.

**Use cases** Full codebase questions, hours-long media, multi-doc merger analysis.

Company-based MCP questions assess platform leadership, not wire protocols alone.

Deployment topology shapes MCP architecture — interviewers test practical product sense.

MCP auth interviews test whether you treat agents as privileged automation — because they are.

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

Hard context engineering trade-offs: prioritization tiers, dynamic reallocation, and what to drop first under pressure.

Cross-language prompt design covering translation drift, code-switching, locale formatting, and eval gaps in multilingual products.

Hard Google company question — document QA with OCR, layout, retrieval, multimodal models, and table/chart grounding.

Scenario question on abstention — retrieval scores, calibration, UX of uncertainty, and avoiding confident wrong answers.

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.

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

Scenario question on classical NLP metrics — what they measure, where they break on paraphrase and factuality, and what to use instead.

Model question on dense vector embeddings — semantic similarity, clustering, classification, dedup, and recommendation beyond vector search RAG.