Top 50 GenAI/LLM Interview Questions (with Friendly, Practical Answers)

December 8, 2025 • Generative AI • LLM • RAG • Prompt Engineering • AI Engineer • System Design • Interview

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You're building with GenAI because you like turning fuzzy ideas into shippable experiences. This guide is for you—the AI engineer or GenAI app developer who wants crisp answers that feel practical, current, and friendly. No trivia. No copy‑paste. Just enough detail to help you shine in interviews and in production.

Tip: If you're short on time, skim the bolded phrases and the "In practice" lines.

Who this is for

• AI engineer interview prep and GenAI app developers (2025)
• Generative AI interview questions and answers with real‑world LLM scenarios
• RAG, prompt engineering, LLM system design, safety, and scalability

Foundations of LLMs

1) What's the difference between a base model and an instruction‑tuned model?

A base model is trained to predict the next token on generic internet-scale data. An instruction‑tuned model is further fine‑tuned (often with human feedback) to follow instructions and produce helpful, safe, and concise outputs. In practice: You'll reach for instruction‑tuned models for apps; use base models when you need full controllability and plan to add your own tuning layer.

2) Why does tokenization matter?

Tokenization breaks text into model-friendly units (tokens). All costs, latency, and context limits are measured in tokens. Different tokenizers change how many tokens your text becomes, affecting cost and truncation. In practice: Budget and prompt design depend more on tokens than characters.

3) How do temperature and top‑p affect outputs?

Temperature controls randomness; higher values produce more diverse text. Top‑p (nucleus sampling) limits sampling to the smallest set of tokens whose cumulative probability ≥ p. In practice: For deterministic tools and evals, use low temperature; for ideation, raise temperature and/or top‑p.

4) What is a context window and why should I care?

The context window is how many tokens the model can see at once (prompt + history + tools + retrieved docs). Large windows reduce truncation but still don't equal "long‑term memory." In practice: If you're chunking or retrieving, design for "right‑sized" prompts—don't flood the model.

5) What is hallucination in LLMs?

Hallucination is confident but incorrect output. It happens when the model fills gaps with plausible text. In practice: Use retrieval (RAG), tool grounding, structured outputs, and targeted evals to reduce it—don't rely on vibes.

6) When do you choose a proprietary model vs. an open‑source model?

Proprietary: strongest performance, long context, turnkey safety, tool support. Open‑source: controllability, privacy, offline, cost. In practice: Start with hosted for speed; move pieces on‑prem or to open‑source as product and privacy needs mature.

7) What is a system prompt and how is it different from a user prompt?

A system prompt establishes role, tone, rules, and constraints; user prompts carry the actual request. In practice: Keep system prompts short, stable, and testable; don't bury product rules deep in user messages.

8) Why do structured outputs matter?

Structured outputs (JSON, XML, Pydantic‑like schemas) reduce parsing errors, enable reliable downstream logic, and make evals easier. In practice: Use JSON mode or a schema tool to eliminate "string surgery."

9) What's the trade‑off between few‑shot and zero‑shot prompting?

Zero‑shot is faster and cheaper but less controllable. Few‑shot gives style and format guidance. In practice: Maintain a small library of task‑specific exemplars; don't overfit your prompt into a novel.

10) How do you think about safety at the prompt layer?

Set boundaries (what to avoid, what to cite), define fallback behavior, include refusal guidance, and apply post‑filters. In practice: Treat safety as a product feature—not just a filter—test it explicitly.

Retrieval-Augmented Generation (RAG)

11) What problem does RAG actually solve?

RAG grounds the model on your domain knowledge without retraining. It reduces hallucinations and keeps answers current. In practice: If the facts aren't in the context, the model is guessing.

12) What are the key steps in a RAG pipeline?

Ingest → chunk → embed → index → retrieve → rank → synthesize. In practice: The "boring" parts—chunking, metadata, indexing—decide your quality.

13) How do you choose chunk size and overlap?

Too large: retrieval misses; too small: context fragmentation. Overlap preserves continuity across boundaries. In practice: Start ~300–800 tokens with ~10–20% overlap, then measure.

14) What's the difference between keyword search and vector search?

Keyword matches exact terms; vector search finds semantic neighbors. In practice: Hybrid search (BM25 + vector) often outperforms either alone.

15) How do you evaluate a RAG system?

Use retrieval metrics (recall@k, MRR), answer faithfulness, and groundedness. Human spot‑checks for top queries. In practice: Build a small, evolving gold set; don't rely on anecdotal wins.

16) How do you prevent data leakage or stale answers in RAG?

Use metadata filters (time, version, access), cache invalidation, and periodic re‑embeddings. In practice: Tie retrieval to permissions; your vector DB is part of your auth surface.

17) When do you re‑embed documents?

When content changes materially, when your embedding model changes, or when evals show drift. In practice: Batch re‑embeddings during off‑peak hours and version your indexes.

18) What is late fusion vs. early fusion in retrieval?

Early fusion combines signals before ranking; late fusion combines independent rankings after. In practice: Late fusion is simpler to ship; experiment before optimizing.

19) How do you mitigate prompt injection in RAG?

Sanitize retrieved text, separate instructions from content, constrain tools, and apply allowlists for model‑callable functions. In practice: Treat retrieved text as untrusted input.

20) What's the role of rerankers?

Rerankers reorder retrieved passages using a cross‑encoder for better precision. In practice: Use lightweight reranking on top results to boost answer quality without huge cost.

Building GenAI Applications

21) What's a good pattern for tool use (function calling)?

Start with tight tool contracts (names, args, constraints), validate inputs, and handle errors deterministically. In practice: Short, explicit tool descriptions beat clever prompts.

22) When should you use an agent vs. a simple chain?

Agents explore and decide; chains execute known steps. In practice: Default to chains for reliability; add agents for open‑ended workflows with strong guardrails and budgets.

23) How do you reduce latency in production?

Use smaller or faster models for simple steps, parallelize independent calls, stream tokens, cache, and pre‑compute embeddings. In practice: Measure p95/p99, not just averages.

24) How do you manage cost?

Right‑size the model per task, limit context, deduplicate prompts, cache results, and use tiered routing. In practice: Model routing (cheap → smart) is your friend.

25) How do you handle rate limits?

Use adaptive retry with jitter, backoff, and concurrency control. In practice: Queue bursty workloads and plan for provider outages.

26) What makes a prompt "production‑ready"?

It's short, explicit, idempotent, versioned, test‑covered, and safe. In practice: Treat prompts like code—review, diff, and roll back.

27) What's a good approach to multi‑turn memory?

Store structured conversation state and summaries outside the model; selectively rehydrate context. In practice: Memory is a product decision, not just a vector store.

28) How do you ensure deterministic formatting (like JSON)?

Use schema‑guided generations, JSON mode, or constrained decoding. Validate and retry on failure. In practice: Prefer "must be valid JSON that matches this schema."

29) How do you design evals for your app?

Define task‑specific metrics (accuracy, completeness, style), build a gold set, run automatic checks, and spot‑check with humans. In practice: Evals should block regressions on deploy.

30) What's your approach to logging and observability?

Log prompts, retrieved docs, tool calls, outputs, costs, and latencies with PII controls. In practice: Good traces turn sporadic bugs into reproducible tickets.

Fine‑Tuning and Adaptation

31) When should you fine‑tune vs. use RAG?

Fine‑tune for style, format, and task‑specific patterns; use RAG for facts. In practice: Many teams do both—RAG for knowledge, fine‑tune for behavior.

32) What data do you need for fine‑tuning?

High‑quality, diverse, instruction‑style pairs with clear inputs, outputs, and metadata. In practice: 2k great examples beat 200k noisy ones.

33) What's LoRA and why is it popular?

LoRA adds small, trainable low‑rank adapters to a frozen model, making fine‑tuning cheaper and faster. In practice: Start with LoRA; only full FT if you truly need it.

34) How do you avoid overfitting during fine‑tuning?

Hold out a validation set, use early stopping, and monitor generalization to new prompts. In practice: Regularly eval against adversarial or out‑of‑domain cases.

35) How do you version and ship a tuned model?

Version datasets, code, hyperparams, and artifacts. Pin model+tokenizer versions. In practice: Treat model releases like app releases with changelogs and rollbacks.

Responsible AI and Governance

36) How do you address bias and fairness?

Measure with representative test cases, mitigate via data balancing and post‑processing, and document known behaviors. In practice: Own the trade‑offs and make impacts visible to stakeholders.

37) How do you implement safety filters?

Layered approach: input filters, system prompt rules, output moderation, and escalation paths. In practice: Fail safely and explain refusals clearly to users.

38) How do you handle privacy and PII?

Minimize collection, mask PII before logging, encrypt at rest/in transit, and honor data retention. In practice: Assume prompts may contain secrets—design for it.

39) What's your approach to model and prompt versioning?

Immutable versions with metadata, gated rollouts, and automatic evals. In practice: Canary prompts + diffed results catch regressions early.

40) How do you prevent prompt injection and data exfiltration?

Separate instructions from content, sanitize retrieved text, constrain tools, apply allowlists, and log tool outputs. In practice: Consider a "policy engine" before tool execution.

Systems, Scaling, and Reliability

41) What's your strategy for high availability?

Multi‑region deployment, provider failover, retries with backoff, and idempotency keys. In practice: Run chaos drills; assume the provider will hiccup on launch day.

42) How do you cache LLM results safely?

Cache by normalized prompt + parameters + model version. Set TTLs and invalidate on data changes. In practice: Beware of caching personalized content without scoping keys.

43) How do you route requests across models?

Policy‑based routing by task, cost, latency, and eval scores. In practice: Put "good enough" small models in front; escalate only when needed.

44) How do you monitor quality over time?

Track win rates, groundedness, escalation rates, hallucination flags, and user feedback. In practice: Quality decays without maintenance—schedule evals.

45) What's the right way to stream responses?

Use server‑sent events or WebSockets, flush regularly, and render partials on the client. In practice: Streaming improves perceived speed and user trust.

Advanced Topics

46) How do you get reliable tool use across multi‑step tasks?

Constrain the toolset, require explicit reasoning steps, enforce JSON schemas, and retry on schema violations. In practice: Reward short, correct plans—not long "thinking" dumps.

47) How do you debug inconsistent outputs?

Log everything, replay exact traces, A/B different prompts/models, and isolate nondeterminism (temperature, top‑p). In practice: Reduce degrees of freedom until it's stable.

48) How do you evaluate hallucinations automatically?

Use groundedness checks (compare to retrieved text), citation matching, and LLM‑as‑a‑judge with careful prompts. In practice: Sample hard cases; automate the boring ones.

49) How do you secure function calling?

Validate input types and ranges, sanitize strings, enforce auth/ACLs, and implement timeouts. In practice: Treat tools like public APIs—zero trust by default.

50) How do you talk about trade‑offs in an interview?

Structure your answer: requirement → options → trade‑offs → decision → risks → mitigation. In practice: Interviewers want your judgment process, not just the "right" tool.

Final tips (for you)

• Keep answers short but grounded in real systems you've built.
• Mention metrics and evals—it signals maturity beyond demos.
• Be explicit about safety, privacy, and cost; they're always on the rubric.
• When you don't know: say how you'd test and iterate. That's senior energy.

You've got this—ship it, learn, and keep a small eval set handy. Good luck! 🚀

FAQ: GenAI/LLM Interview (2025)

What are the most asked Generative AI interview questions in 2025?

Expect fundamentals (tokenization, context windows, temperature/top‑p), RAG design, prompt engineering, structured outputs (JSON), safety, and cost/latency trade‑offs—plus real incident stories about hallucination mitigation and evals.

How do I prepare for an AI engineer interview focused on LLMs?

Practice short, grounded answers with metrics. Be ready to explain RAG vs. fine‑tuning, model routing, caching, evals, and privacy/PII handling. Bring a simple architecture sketch for your favorite use case.

Which topics improve my chances of passing senior interviews?

Retrieval quality, schema‑constrained outputs, observability (tokens/latency/costs), safety (prompt injection, refusal policy), and rollout discipline (versioning, canary, eval gates).

What keywords should my resume/projects highlight?

"RAG," "prompt engineering," "structured outputs," "vector search," "model routing," "observability," "privacy/PII," "cost optimization," and "LLM system design."

What is tokenization (and why it matters)?

Brief: Tokenization turns text into model-readable tokens; costs, latency, and limits are all measured in tokens.

What it solves: Predictable budgeting and latency, consistent handling across languages/symbols, and reliable chunking for retrieval.

Use cases:

• Estimating cost/latency before launch by counting tokens in prompts and outputs.
• Designing chunk sizes and overlaps in RAG to prevent truncation.
• Picking a tokenizer that's efficient for your domain (e.g., code vs. prose).

What are context windows?

Brief: The context window is how many tokens the model can "see" at once (prompt + chat history + tools + retrieved docs).

What it solves: Lets you provide relevant history and facts without retraining; forces prioritization when inputs are long.

Use cases:

• Sliding‑window memory: keep the last N turns and a rolling summary for older context.
• RAG: pass only top‑k passages and essential metadata rather than entire documents.
• Long‑form tasks: map‑reduce summaries or section‑level Q&A to stay within limits.

How do temperature and top‑p work (and when to use them)?

Brief: Temperature controls randomness; top‑p restricts sampling to the most probable subset of tokens (by cumulative probability).

What it solves: Balances creativity vs. consistency, helping you stabilize automations or encourage ideation.

Use cases:

• Low temperature/top‑p for deterministic tasks (extraction, classification, JSON outputs).
• Moderate settings for grounded Q&A where tone and phrasing matter.
• Higher settings for brainstorming, copywriting, or variant generation/A‑B tests.

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