Glean RAG & Agent System Design interview questions

Pure vector search fails on the exact queries enterprise users ask most: IDs, error codes, product names. Here's the failure-mode framing that turns a definition question into a design answer.

A reported FDE design question that punishes one-size-fits-all answers. Each corpus has a different 'semantic unit': get the three designs, the parsing tools to name, and the eval that proves the split was worth it.

The retrieval design question with a precise rubric: candidates who can say what each stage rescues, and what it costs in latency, clear it. Includes the RRF detail and the latency budget interviewers ask for.

The modal FDE design question of the last two years. There's a scoring trap in the first 60 seconds, and most candidates jump to fixes and fail. Here's the diagnostic tree that wins it.

Three plausible fixes, one decision framework. Interviewers grade the ordering (cheapest-reversible first) and whether you can say what each option costs in data, time, and operational burden.

Citations are the trust interface of enterprise RAG, and the easiest thing to fake badly. Covers inline citation generation, span verification, and the verification-rate metric that interviewers actually want.

An Anthropic-signature integration question. The hidden rubric: tools are a curated product surface, not a proxy for your OpenAPI spec, plus the auth model and confused-deputy trap most candidates miss.

A Glean-signature design question where deletions, not ingestion, are the trap. Covers the connector framework, change detection per source class, tombstones, and the freshness SLO customers actually sign.

Glean's signature design question. The two traps: post-filtering that drains your top-k, and the leak through generated answers. The winning architecture filters at retrieval, and here is how, at scale.

The POC-to-production gap is the FDE's home turf. What actually breaks at each order of magnitude (index, ingestion, latency, cost) and the dependency math that makes 99.9% harder than it sounds.

A real interview question with a trap in its framing: it offers you two expensive options and hides the cheap one. The escalation ladder from feature-based reranking to two-tower fine-tuning, with the cold-start and position-bias answers ready.

A deceptively short interview question that's really a trap: embeddings cannot do exact match, and the interviewer knows it. What to actually turn on (payload full-text indexes, sparse vectors, metadata filters) in the vector DBs you already run.

The classic 'it regressed and the prompt is untouched' triage. The answer that scores walks a decisional tree from retrieval to generation and names the silent culprit most candidates miss: the embedding stack changed under you.

Golden sets catch what you anticipated. The harder problem is the live traffic you never labeled. The answer that scores layers cheap proxy signals against a small anchored golden set and knows what each can and cannot tell you.

A multi-hop question over a property graph is a query-generation problem, not a similarity-search one. The staff-level move is grounding generation in the live schema and closing an auto-healing loop on the DB's own error messages.

Self-correcting RAG is easy to demo and easy to ruin. The discipline is a gate that grades evidence before drafting, a termination rule that is not just a loop cap, and a metric that proves correction adds facts rather than fluff.

Slides are the worst RAG corpus: text in shapes, meaning in charts, the message in the image. A query like 'the blue bar chart with Q3 revenue' fails if you embed only the extracted text, and citing the wrong slide number kills trust instantly.

The hard part is not the retrieval pipeline, it is the boundary. Embeddings and the index live inside the VPC; the only question is whether the generation model is an in-VPC open model or an external API with a zero-retention DPA, and what crosses that line.

The warm-up that filters out tutorial-level candidates. Interviewers aren't grading the diagram, they're grading whether you know the three places real pipelines fail and how you'd see it happening.

Everyone says '500 tokens with overlap.' The candidates who get hired explain why chunk boundaries are a recall problem, name three strategies, and describe the experiment that picks between them.

'Evals first' is the #1 hiring signal in FDE design rounds. This is the foundational version: what a 50-case golden set looks like, which metrics split retrieval from generation, and the launch gate that convinces interviewers.

Text-to-SQL's nightmare isn't syntax errors, it's the query that runs, returns a confident number, and is silently wrong. The defense is a semantic layer plus transparency UX. Here's the full design.

Arguing against agents reads senior; it's an explicit rubric signal at AI labs. Learn the cost ladder from single call to autonomous loop, and the three conditions that actually justify an agent.

Sierra's signature design exercise. The grading hinges on three things most candidates underweight: tool API shape, the read/write trust boundary, and eval cases written before the agent. Walkthrough inside.