RAG & Agent System Design is a core part of the Google Forward Deployed Engineer loop. Retrieval pipelines, chunking, reranking, tool-using agents, guardrails, multi-tenancy and eval harnesses: the modal FDE design round at OpenAI, Anthropic, Sierra, Glean and Scale. Below are the rag & agent system design questions to prepare, the ones tagged to Google first, then the highest-signal questions from our RAG & Agent System Design track, each with an answer written to a senior-engineer bar.

WHAT GOOGLE LOOKS FOR HERE · Google Cloud significantly expanded its Forward Deployed Engineer program in 2026 to move enterprise customers from AI pilots to production; expect RAG, vector databases, and production GenAI deployment depth. See the full Google interview process →

RAG & Agent System Design questions tagged to Google

More RAG & Agent System Design questions for Google's loop

The highest-signal rag & agent system design questions candidates rate most useful, modeled on what Google's Forward Deployed Engineer loop tests.

Concepts behind Google's RAG & Agent System Design round

The vocabulary and mental models these questions assume. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

FoundationalRetrieval-Augmented Generation (RAG)RAG grounds a language model in your own data by retrieving relevant passages at query time and putting them in the prompt, so the model answers from real sources instead of memory. It is the default pattern for almost every enterprise FDE deployment, which is why nearly every loop tests it.

FoundationalVector DatabasesA vector database stores embeddings alongside metadata and answers nearest-neighbor queries fast using approximate indexes. The real interview question is not how they work but when you actually need one instead of a library or plain Postgres with pgvector.

CoreSign inHybrid Search (Lexical + Vector)Hybrid search runs a keyword retriever (BM25) and a dense vector retriever side by side, then merges their result lists, because each one misses cases the other catches. Vectors lose exact codes and rare jargon, BM25 loses paraphrase, and combining them with Reciprocal Rank Fusion usually beats either alone.

CoreSign inChunking StrategiesChunking is how you split documents into the units you embed and retrieve, and it quietly sets the recall ceiling for your entire RAG system. Get the size, boundaries, and metadata wrong and no reranker or prompt can recover the answer that never got retrieved.

CoreSign inReranking and Two-Stage RetrievalTwo-stage retrieval pairs a cheap recall-heavy first stage that pulls dozens of candidates with a precise reranker that re-scores each one against the query. It is the standard fix when vector search returns relevant-ish chunks but the right one is not in the top few, and it trades a little latency for a lot of precision.

CoreSign inApproximate Nearest Neighbor (ANN)Brute-force vector search is O(N*d) per query and falls apart at millions of vectors, so ANN trades a sliver of recall for orders-of-magnitude speed. The two dominant families are IVF (cluster then probe nearby cells) and HNSW (walk a navigable graph), with product quantization to shrink memory. The non-negotiable habit is measuring recall@k against a brute-force baseline.

FoundationalAI Agents and Tool UseAn agent is a language model wrapped in a loop that lets it choose tools, act, observe the result, and decide what to do next. The skill interviewers test is judgment: knowing when that loop earns its unpredictability and when a fixed pipeline is cheaper, faster, and safer.

Advanced🔒 PremiumAgent MemoryAgent memory is how an agent carries state across turns and sessions. Short-term memory is the conversation and scratchpad living inside the context window, bounded and expensive. Long-term memory is an external store the agent writes to and retrieves from on demand, usually via RAG, so it can recall facts from last week without holding them in the prompt. FDE loops probe this because the hard parts, summarization, what to persist, and stale or contradictory memory, are where agents quietly break.

Google's Forward Deployed Engineer loop draws rag & agent system design questions such as "A maintenance assistant cites the right equipment family but the wrong manual revision. Fix the retrieval contract.", "An insurance document assistant summarizes every page it sees but misses an absent attachment. Design the check.", "Walk me through a RAG pipeline end-to-end, and tell me where it usually breaks". Retrieval pipelines, chunking, reranking, tool-using agents, guardrails, multi-tenancy and eval harnesses: the modal FDE design round at OpenAI, Anthropic, Sierra, Glean and Scale. The full set, ordered easy to hard with expert answers, is below.

Work the rag & agent system design questions on this page in order, study the underlying concepts linked below, then review Google's full interview process for how this round fits the loop. For Google specifically, interviewers weight this: Google Cloud significantly expanded its Forward Deployed Engineer program in 2026 to move enterprise customers from AI pilots to production; expect RAG, vector databases, and production GenAI deployment depth.

Yes, at scale. Google Cloud runs a large, multi-level Forward Deployed Engineer program, alongside Customer Engineers and Applied AI roles, embedding engineers with enterprise customers to ship agentic solutions on Vertex AI and Gemini Enterprise. It is one of the biggest FDE recruiters in 2026.

Expect practical coding, GenAI and agent system design, and role-related rounds on taking a Gemini or Vertex AI solution from proof of concept to production, plus customer-facing judgment. Interviewers probe how you scope ambiguous enterprise problems and build reliably.

Other Google interview rounds

The other tracks Google's Forward Deployed Engineer loop tests.

Prep the whole Google Forward Deployed Engineer loop

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