Harvey RAG & Agent System Design interview questions

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.

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.

Calibrated abstention is a system property, not a prompt line. Learn the four abstention signals, the precision-at-coverage framing, and why 'just tell it to say I don't know' fails the interview.

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.

Harvey's signature design question. The twist: the architecture is the easy half. The graded half is designing for reviewer trust, where one hallucinated clause costs more than a hundred correct ones earn.

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.

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.

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.

'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.

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.

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.

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.

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.

A Sierra-signature scenario where 'put it in the system prompt' scores zero. The winning answer is defense-in-depth with a measured catch-rate per layer, plus an honest number for what still gets through.

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.

Perceived latency is a design surface, not an infrastructure detail. Covers progressive disclosure, SSE vs WebSockets, resumability, and the mid-stream failure question most candidates have never thought about.

The unglamorous design question that exposes who has run LLMs in production. Batch APIs at 50% off, idempotency keys, poison records, spend kill-switches: the full checklist interviewers listen for.

The staff-level version of 'evals first.' Four layers, each with a job, a cost, and a blind spot, plus judge calibration numbers and the data flywheel that ties production failures back into CI.

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.