Mistral LLM & GenAI Fundamentals interview questions

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.

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 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.

10x doesn't come from one trick. It's multiplicative: routing x caching x prompt diet x batching x distillation. The stacked-levers playbook with rough multipliers, and the eval gate that keeps quality honest.

Adapter math, the VRAM arithmetic that makes 4-bit a 4x unlock, and the honest decision rule for on-prem deployments. The deep-internals question Mistral and Cohere loops use to find real practitioners.

The GPU-utilization math that decides this argument, the hidden line items (ops headcount, quality gap, model churn), and the hybrid recommendation that wins both the analysis and the customer.

Two compression levers with very different bills. The arithmetic that decides between them, when to stack both, and the eval discipline that keeps a 'cheaper' model from quietly tanking your hardest 10% of queries.

Rotary embeddings encode position as a rotation of the query and key vectors, so attention scores depend only on the gap between two tokens. That relative property is why RoPE generalizes past its training length where learned absolute tables fall off a cliff.

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.

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.

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.

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.

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.

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.

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.

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.

Malformed arguments, wrong tool, hallucinated parameters, retry loops: the full failure taxonomy plus the production defenses (validation, idempotency, capped retries) that separate builders from readers.