rag

The single most-reported FDE conceptual question across every AI lab. Interviewers don't want definitions; they want the decision framework, the cost math, and the recommendation order that reads senior.

Truncate? Summarize? Rerank? Map-reduce? A production problem every RAG deployment hits, with a menu of fixes interviewers expect you to weigh, plus the diagnostic question to ask first.

Models read the start and end of your context far better than the middle, a measured effect with direct consequences for chunk ordering, k, and prompt layout. What to change and how to prove it.

In high-stakes domains a wrong answer is worse than no answer, but a system that always abstains is useless. The selective-prediction design, the layered abstention triggers, and the two metrics that govern the tradeoff.

Million-token windows didn't kill RAG; they changed when you reach for it. The decision rule that holds up in production, and the silent failure each option hides behind a confident answer.

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.

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 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.

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.

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.

Reciting the four definitions is table stakes; the differentiator is knowing which two need ground truth, what each dimension tells you to fix, and where the judge-model scores quietly lie. The 2x2 that turns RAGAS into a debugging tool.

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.

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.

A 500-page doc is well over 200k tokens, and even if it fit, stuffing it in would rot the context. The real design is a working set in context plus an external memory the agent retrieves from and compacts into as it goes.

Deep-research agents run unsupervised for twenty minutes and their report goes straight to an executive. The failure mode is not a crash, it is confident citations that do not support the claims. Most candidates design the retrieval and skip the verification pass, the run budget, and the freshness story.

The eval set says recall fell 2.3 points, which is inside the noise you would expect from a re-index. Support says the product got worse. Both are right, and the reason is that one document class lost 49 points of recall while everything else improved slightly.

Recall@10 is 0.94 and climbing, and answer accuracy is going down. The two facts are consistent, and reconciling them is the whole question: every passage you add to raise recall also adds a distractor the generator has to survive.

The document count matches, nothing threw, and a specific set of documents is now unreachable by any query. That combination narrows the cause fast, and the two survivors are an approximate-search parameter that a rebuild reset and a metadata filter applied in the wrong order.

A reranker cannot promote a document the retriever never returned, which makes candidate-set recall a hard ceiling on the entire exercise. Compute the ceiling and the disappointment becomes arithmetic: three points of headroom were available, twelve were sitting upstream, and the latency was an implementation detail.

The coding question that doubles as a RAG-fundamentals check. Greedy packing is easy; the oversized-sentence case, the overlap-progress trap, and the tests you write unprompted are what AI-lab interviewers actually score.

The signature coding problem at a legal-AI company, and it is interval merging wearing a product costume. The candidates who struggle are the ones who start matching before deciding what a match even is.

Five LLM agents over a hundred steps, and the bar is zero contradictions with a 500-page contract. The real test is the state and grounding layer, not the agent prompts.

Graded on two things most engineers never practice: the quality of the analogy and knowing when to stop. Here's a two-minute script that lands with a legal exec, and the depth trap to avoid.

The defining trusted-advisor dilemma, and both pure compliance and pure stubbornness fail it. The strong move is a sequence: diagnose, recommend in writing, then a test that lets the evidence decide.

The most common real ticket in applied AI, and it contains almost no information. What you do in the first hour decides whether you spend a day on this or three weeks.

The four-point redaction design is becoming the standard FDE privacy round. Most candidates fortify the chat box and lose on the other three doors, here's the full design, including the de-redaction map that makes it usable.

The defining security design problem of enterprise RAG, and the reason Glean exists. Query-time ACL filtering, permission sync lag, and the leakage channels that survive naive designs: caches, citations, and the model itself.

Authentication is not isolation: a request can be fully authenticated to tenant A and still touch tenant B through a shared index, a semantic cache, or a fine-tuned model. The strong answer names every shared surface and how you stamp tenant identity through all of them.

Cross-tenant or cross-user data exposure through a model is the breach that starts regulatory clocks. The runbook has to answer 'whose data, to whom, how many' from logs that most teams never designed for, and contain a leak that may live in a cache or a fine-tuned model, not a deploy.