Glean LLM & GenAI Fundamentals interview questions

The concept underneath every RAG system you'll ever deploy. How to explain vectors, cosine similarity, and the one limitation of embeddings that costs real deployments accuracy.

The same operation scores attention and ranks your RAG results. Knowing exactly what the dot product measures, and when its magnitude term quietly breaks your retrieval, is what separates a working pipeline from a mysteriously bad one.

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.

Cosine similarity finds topically close, not correct. The failure taxonomy behind most RAG accuracy complaints, and the hybrid-search + reranker fix stack interviewers expect you to know cold.

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.

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

A deceptively simple sampling question with one trap (does temperature 0 fix hallucinations?) and one senior move interviewers reward. Know which workflows want zero.

A classic metrics question with a domain twist. The answer isn't 'recall'; it's a cost-of-errors argument plus a threshold strategy, and interviewers grade the reasoning, not the metric you pick.

JSON mode, strict schemas, constrained decoding, validate-and-retry: the escalation ladder for structured output, plus the catch nobody mentions: schema-valid can still be factually wrong.

Anyone can list mitigations; the question is really about costs: latency, dollars, and lost recall. The three-layer answer with honest price tags that AI-lab interviewers score highest.

TTFT vs tokens-per-second, the output-length lever everyone forgets, and why streaming is the highest-ROI fix that changes no latency at all. The five-lever answer with real numbers.

Why a raw base model answers your question with three more questions, what RLHF actually buys, and the niche cases where customers should pick base. A tidy answer most candidates fumble.

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.

The data-volume thresholds, the break-even math, and the regression-prevention checklist that turn 'just fine-tune it' into a defensible engineering decision. Includes the crossover rule interviewers listen for.

Zero retention, private endpoints, redaction pipelines, or open weights in their VPC, the four-rung ladder for PII-sensitive deployments, and the discovery questions that pick the right rung.

A fixed 1000-token budget forces the search choice. Step-level beam search guided by a process-reward model beats best-of-N and MCTS on accuracy per token for math, and you collect the PRM labels automatically with Monte-Carlo rollouts, no human step annotation.