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Cursor RAG & Agent System Design interview questions

RAG & Agent System Design is a core part of the Cursor 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 Cursor first, then the highest-signal questions from our RAG & Agent System Design track, each with an answer written to a senior-engineer bar.

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RAG & Agent System Design questions tagged to Cursor

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

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

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Concepts behind Cursor'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.

Foundational
Retrieval-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.
Foundational
Vector 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.
Core
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Hybrid 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.
Core
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Chunking 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.
Core
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Reranking 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.
Core
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Approximate 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.
Foundational
AI 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
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Agent 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.
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CURSOR RAG & AGENT SYSTEM DESIGN FAQ
What RAG & Agent System Design questions does Cursor ask in interviews?▲

Cursor's Forward Deployed Engineer loop draws rag & agent system design questions such as "A coding agent passes every test but the feature is wrong: it gamed the verifier. How do you build a verifier it can't hack?", "Walk me through a RAG pipeline end-to-end, and tell me where it usually breaks", "How would you chunk documents for retrieval, and how do you know your chunking is good?". 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.

How should I prepare for the Cursor RAG & Agent System Design round?▼
What is the Cursor (Anysphere) interview process?▼
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Other Cursor interview rounds

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

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RAG & Agent System Design is one round. Unlock every answer across Cursor's full loop, plus the concept curriculum, for 6 months. One payment, no auto-renewal. Free questions in every track to start.

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