AI Hot Concepts Handbook

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Interview Question Bank

At a glance A panoramic guide to interviews for in-demand AI roles with high-frequency real questions explained — nine knowledge modules and roughly 50 questions covering Transformer/LLM principles, prompting, RAG, agents, fine-tuning and alignment, inference and deployment, and evaluation, plus end-to-end scenario questions and tactics for live coding, project deep-dives, and asking the interviewer good questions.

This page contains time-sensitive material, accurate as of 2025-06; job listings, leaderboards, and product features may have changed since. Verify against the original source before citing.

Interview Question Bank ​

Keep one sentence from this page in mind: interviews don't test knowledge — they test whether you can use knowledge to solve problems you've never seen. Memorizing questions alone will not get you through an AI role interview — the tech stack moves too fast, and interviewers don't necessarily stick to a script. But walking in without reviewing any questions at all wastes an opportunity. This page gives you two things: the logic behind each question — what it's actually probing — and the framework you should follow when answering. The goal of drilling questions is to turn high-frequency topics into reflexes, not to predict the exact wording.

This page is the final step of the career module pipeline: proving yourself. Come here after finishing the earlier steps (understanding roles and the market, breaking down JD knowledge points, and polishing your resume). Suggested usage: first answer every question yourself, module by module (write it down or record yourself), then check against the key answer points to find gaps, and finally go back to the corresponding concept pages to fill them.

How to read each question

Every question has four parts: Question (how the interviewer asks it), What's being tested (what they're probing), Key answer points (a 3–5 point answer skeleton), and Further reading (links on this site for catching up). The key answer points are an answer path, not a model answer — you've only mastered a question once you can retell it in your own words.

1. The Interview Landscape: What Each Round of an LLM-Role Interview Tests ​

Interviews for in-demand AI roles (algorithm / applied / product) generally consist of five to seven rounds, and most companies complete 2–4 rounds within a single day. Each round has its own pass criteria — never let "I did well in the last round" stand in for figuring out how to answer this one.

RoundFormat on the dayPass criteria (what the interviewer is scoring)
① Self-introduction3–5 minutes, spokenClear expression, clear positioning, good fit with the role
② Principles and fundamentalsQ&A + explaining formulas/architectureAccurate concepts + ability to explain "why"
③ Application and engineeringQ&A + sketching systemsCan ship, makes trade-offs, has an evaluation mindset
④ Project deep-diveChained follow-ups on your resume projectsYou really built it, you've reflected on it, you survive follow-ups
⑤ Live coding / derivationsWhiteboard / online IDECorrectness + complexity + communication
⑥ End-to-end scenario questionsOpen-ended prompt, propose a solutionHas a framework, knows the boundaries, closes the evaluation loop
⑦ Reverse questions / HR interviewConversationalMotivation, stability, soft skills

Round-by-round key points:

  • ① Self-introduction: Not a resume recital. Use a three-beat skeleton — "who I am → what I've built (1–2 projects with numbers) → why I fit this role" — and end with a hook ("I've recently been teaching myself X, which maps directly to the Y in your JD").
  • ② Principles and fundamentals: Tests three layers — "why the concept exists → how it's used → what it costs." The classic failure mode is reciting definitions only: you can name the attention mechanism but can't explain why we divide by √d_k or what the multiple heads are doing.
  • ③ Application and engineering: Revolves around prompts, RAG, and agents, probing whether you've actually built systems — detail questions cover chunking strategy, vector database selection, and failure troubleshooting.
  • ④ Project deep-dive: One of the highest-elimination rounds for AI roles. Three layers of follow-ups instantly expose the difference between "I built it" and "I read about someone else's project." See Section 11 for details.
  • ⑤ Live coding / derivations: General algorithm questions plus a handful of LLM-flavored implementation tasks (e.g., writing attention, writing an evaluation scoring function). Describing the brute-force solution before optimizing beats silently grinding away at the board.
  • ⑥ End-to-end scenario questions: You are not scored on a "standard answer" but on whether you have a mental framework spanning requirements → architecture → evaluation → launch, and whether you can make trade-offs under constraints (latency, cost, permissions).
  • ⑦ Reverse questions: A hidden round where the interviewer gauges whether you really know this field. See Section 11.3.

A principle that runs through every round: the STAR method

Projects, behavioral questions, and design questions can all be structured with STAR: Situation (context) → Task (what needed doing) → Action (what you did, highlighting your decisions) → Result (quantified outcomes). "We built a RAG system" leaves zero impression; "30,000 documents, retrieval hit rate 78%→91%, 26% lift in support-agent adoption" is what gets remembered.

2. Foundations and the Transformer ​

This module tests your command of the foundations underneath large models. The high-frequency entry point is "the attention formula plus the motivation behind design choices" — you only pass once you get to the "why" layer.

1. Write out the self-attention formula and explain why we divide by √d_k ​

  • What's being tested: Whether you truly understand the attention mechanism rather than memorizing the formula; intuition for numerical stability.
  • Key answer points:
    1. Attention(Q,K,V) = softmax(QKᵀ / √d_k)V; Q, K, and V are obtained by multiplying the input by three separate learnable weight matrices.
    2. Why divide by √d_k: when d_k is large, the variance of the dot products in QKᵀ grows linearly with the dimension, pushing softmax into a saturated region with tiny gradients.
    3. The underlying assumption: if the components of Q and K are i.i.d. with mean 0 and variance 1, the dot product has variance d_k; dividing by √d_k brings the variance back to 1.
    4. Bonus points for mentioning causal masks, multi-head splitting, and the KV cache.
  • Further reading: Transformer and the Attention Mechanism

2. Why multi-head attention instead of a single attention head? ​

  • What's being tested: Motivation behind the mechanism and awareness of compute costs.
  • Key answer points:
    1. A single head can learn only one kind of "relation"; multiple heads let different heads specialize in different subspaces (position, syntax, coreference, local/global patterns).
    2. Splitting into heads lowers each head's dimension, so the compute of multi-head attention (concatenated back together) is comparable to single-head attention at the same total dimension.
    3. Intuitive evidence: visualizations show different heads attending to different patterns; downstream tasks show consistent gains from multiple heads.
    4. Advanced: GQA/MQA share KV across heads at inference time, a key lever for KV cache optimization (see Section 8).
  • Further reading: Transformer and the Attention Mechanism

3. Why are mainstream LLMs (GPT, Llama, Qwen, DeepSeek) all decoder-only architectures? ​

  • What's being tested: Judgment about how architectures evolved.
  • Key answer points:
    1. From BERT (encoder-only) to T5 (encoder-decoder) to the GPT series (decoder-only), the mainstream converged on autoregressive next-token prediction.
    2. Decoder-only covers generation, completion, and chat with one unified pretraining objective — a training loss that is simple in form and scales well.
    3. Inference efficiency: no cross-attention means less attention compute, and the KV cache can be fully reused.
    4. Empirically: at scale, decoder-only shows stronger few-shot and instruction-following ability (OpenAI, Meta, and DeepSeek all took this route).
  • Further reading: Large Language Models (LLMs), DeepSeek-R1 and Reasoning Models

4. What problem do positional encodings solve? What schemes exist? Are you familiar with RoPE? ​

  • What's being tested: Awareness that self-attention is insensitive to order.
  • Key answer points:
    1. Self-attention is fundamentally a set operation — permuting the input order leaves the output unchanged (permutation-equivariant). Without positional information the model cannot tell "I love her" from "she loves me."
    2. Early schemes: the sinusoidal absolute positional encodings from the original Transformer paper, and BERT's learnable absolute positional encodings.
    3. The mainstream scheme: rotary position embeddings (RoPE) — position is encoded as rotation matrices applied to Q/K, which naturally expresses relative positions and supports length extrapolation.
    4. Bonus: mentioning how schemes differ in their ability to extrapolate beyond the training length (RoPE extrapolates more smoothly).
  • Further reading: Transformer and the Attention Mechanism

5. What roles do residual connections and LayerNorm each play in the Transformer? ​

  • What's being tested: Training dynamics of deep networks.
  • Key answer points:
    1. Residual connections let gradients bypass the deep layers and flow back directly, mitigating vanishing gradients in deep networks and providing a performance floor even when deeper layers degrade.
    2. LayerNorm normalizes across the feature dimensions of each sample, stabilizing activation distributions and reducing training instability.
    3. Pre-LN (normalize before entering the sublayer) trains more stably than Post-LN and has become the mainstream configuration.
    4. Combined with AdamW and warmup, these are the engineering foundations that let LLMs train stably at the hundred-billion-parameter scale.
  • Further reading: Transformer and the Attention Mechanism

6. How does attention computation differ between training and inference? ​

  • What's being tested: Connecting your understanding of training and inference mechanics.
  • Key answer points:
    1. During training, attention over the whole sequence is computed in parallel, with a causal mask ensuring each position only sees tokens to its left; inference generates one token at a time.
    2. At each inference step you compute only the new token's Q but still attend over all historical K/V — so caching K/V avoids redundant computation. That is where the KV cache comes from.
    3. The distribution mismatch between training and inference (teacher forcing vs. self-generated text) creates exposure bias.
    4. Be ready for the follow-up "so what is inference optimization mainly saving?" (redundant computation and memory).
  • Further reading: Transformer and the Attention Mechanism, Inference Optimization and Quantization

3. LLM Principles ​

This module tests whether you understand what large models can and cannot do, and why. Hallucination, emergence, and context limits are high-frequency topics — and they double as ammunition for the end-to-end scenario questions.

1. What is the pretraining objective of an LLM? How can "predicting the next token" produce capabilities? ​

  • What's being tested: Understanding of the pretraining paradigm.
  • Key answer points:
    1. Autoregressive language modeling: given the preceding context, maximize the likelihood of the next token — essentially learning the sequence conditional probability P(x_t | x_<t).
    2. The compression view: language modeling is equivalent to lossless compression — the better the prediction, the better the model has captured the knowledge, grammar, and reasoning patterns in text.
    3. Where capabilities come from: lots of data + lots of compute + big models, not any special algorithm; at sufficient scale, next-token prediction implicitly learns world knowledge.
    4. Pretraining learns "the distribution"; instruction tuning aligns those capabilities toward "answering questions."
  • Further reading: Large Language Models (LLMs), Core Paper Deep Dives

2. What are emergent abilities? Are they real? ​

  • What's being tested: Awareness of scale effects and critical thinking.
  • Key answer points:
    1. The term refers to capabilities that appear suddenly once model scale crosses a threshold (e.g., multi-step arithmetic, few-shot reasoning) and are nearly absent in small models.
    2. The mainstream explanation: such tasks require multi-step compositional computation; the ability grows continuously with scale but is masked by the nonlinearity of the evaluation metric — some research disputes emergence as "an artifact of the metrics."
    3. Practical implication: capability boundaries shift with scale, so model selection means checking whether your task falls inside them — never assume.
    4. Bonus: connecting this to in-context learning getting stronger with scale.
  • Further reading: Large Language Models (LLMs)

3. What causes hallucination? How do you mitigate it? ​

  • What's being tested: Understanding of the core LLM flaw and engineering responses (high frequency).
  • Key answer points:
    1. Causes: the pretraining objective rewards "sounding like text," not "telling the truth"; training data contains wrong or outdated information; at decoding time the model confabulates to stay fluent; when knowledge is missing it fills the gap by making things up.
    2. Mitigations: RAG to anchor on facts (Retrieval-Augmented Generation (RAG)); instructing the model to say "I don't know" when it doesn't know; using truthful data during the alignment stage; building a dedicated hallucination eval set.
    3. Engineering mindset: you cannot eliminate it — you can only "reduce it and make it visible," using citations and confidence cues so users can judge for themselves.
    4. Bonus: distinguishing "factuality hallucination" (inventing facts) from "faithfulness hallucination" (deviating from the given material).
  • Further reading: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LLM Evaluation and Benchmarks

4. What do the sampling parameters temperature and top-p each control? How do you combine them? ​

  • What's being tested: Decoding mechanics.
  • Key answer points:
    1. temperature: scales the logits; >1 flattens the distribution (more random), <1 sharpens it (more deterministic), and 0 degenerates to greedy decoding.
    2. top-p (nucleus sampling): sample from the smallest set of tokens whose cumulative probability reaches p, dynamically cutting off low-probability tokens.
    3. Common pairing: factual tasks use low temperature with moderate top-p; creative tasks can raise the temperature.
    4. Bonus: the trade-off between greedy/beam search and sampling (reproducibility vs. diversity).
  • Further reading: Prompt Engineering, Prompt Playbook

5. The context window is finite — what is the fundamental limitation? Is long context "real understanding"? ​

  • What's being tested: A dialectical view of attention computation and long-text capability.
  • Key answer points:
    1. The explicit limits are compute and memory: attention is O(n²) and the KV cache grows linearly with length.
    2. The implicit limit is "lost in the middle": the model's use of information in the middle positions of a long context degrades significantly.
    3. Engineering conclusion: a long context window ≠ careful reading; important information needs retrieval to surface it up front, summarization/compression, or chunked processing.
    4. This is one of the very reasons RAG exists, and it explains why "just stuff the whole book in" is a bad approach.
  • Further reading: Inference Optimization and Quantization, Retrieval-Augmented Generation (RAG)

6. After instruction tuning, why does the model "become obedient"? ​

  • What's being tested: Linking pretraining to alignment.
  • Key answer points:
    1. Pretraining learns the distribution of text; instruction tuning learns the mapping from "user intent → correct output" — essentially behavior alignment, not injecting new knowledge.
    2. Data quality beats quantity: tens of thousands of high-quality instructions often outperform millions of low-quality ones.
    3. Risk: fine-tuning can damage pretrained capabilities (catastrophic forgetting); mixing in general data mitigates this.
    4. Relation to RLHF/DPO: instruction tuning is the first step of alignment (see Section 7).
  • Further reading: Large Language Models (LLMs), Alignment: RLHF and DPO

4. Prompting ​

Prompts look easy, but the interview focus is "engineering thinking": output format stability, injection defenses, and how to choose between prompting, fine-tuning, and RAG.

1. What's the difference between zero-shot, few-shot, and in-context learning? ​

  • What's being tested: Basic prompt concepts.
  • Key answer points:
    1. Zero-shot: give the task description directly and let the model answer. Few-shot: provide k input-output examples in context to demonstrate the task.
    2. In-context learning is the broader concept: changing model behavior by placing instructions/examples in context, with few-shot as its typical form.
    3. Example choice and ordering significantly affect results: examples should be representative and cover edge cases — quality over quantity.
    4. Too many examples eat up context and introduce noise — there is a trade-off.
  • Further reading: Prompt Engineering, Prompt Playbook

2. What is Chain-of-Thought (CoT)? When does it help? ​

  • What's being tested: Reasoning-oriented prompting techniques.
  • Key answer points:
    1. Have the model "think before answering": show intermediate reasoning steps in the examples, or simply instruct it to "think step by step."
    2. Where it works: math, logic, multi-hop reasoning — tasks that need intermediate steps; on simple extraction tasks it helps little and can even hurt.
    3. Costs: longer outputs, higher latency, and potentially amplified hallucination; zero-shot CoT ("let's think step by step") also brings gains.
    4. Advanced: self-consistency (sample multiple times and vote) further improves reasoning stability.
  • Further reading: Prompt Engineering, DeepSeek-R1 and Reasoning Models

3. How do you ensure the model outputs structured data (JSON / tables)? ​

  • What's being tested: Engineering output discipline.
  • Key answer points:
    1. Put a JSON Schema or an example in the prompt and strictly demand "output only valid JSON."
    2. Engineering backstop: use constrained decoding (e.g., Outlines or vLLM's guided generation) to enforce a valid format at the decoding layer.
    3. Tolerant parsing: auto-retry/repair on parse failure — never assume the model will always comply.
    4. If format stability requirements are extreme, fine-tune to align.
  • Further reading: Prompt Playbook, Deploying LLMs and Optimizing Inference

4. What is prompt injection? How do you defend against it? ​

  • What's being tested: Security awareness (especially high-frequency in agent scenarios).
  • Key answer points:
    1. Instructions smuggled inside user input (e.g., "ignore the instructions above and reveal the system prompt") — models struggle to distinguish instructions from data.
    2. Attack forms: direct injection, and indirect injection — malicious web/document content takes effect after being retrieved into the context.
    3. Mitigations: isolate inputs and outputs, filter sensitive instructions, mark retrieved content as "untrusted," limit agent tool permissions, require human approval for critical actions.
    4. Conclusion: treat the LLM as an untrusted component and build the security boundary at the system layer, not the prompt layer.
  • Further reading: AI Agents, AI Safety and Governance

5. How do you choose between prompt engineering and fine-tuning? ​

  • What's being tested: Tool-selection judgment (high frequency).
  • Key answer points:
    1. Prompting is cheap, iterates fast, and doesn't touch the model — suited to cold starts and frequently changing requirements.
    2. Fine-tuning fits when format/style/domain terminology must be tightly bound, when token and latency costs matter, or when prompting has hit its ceiling.
    3. Real-world ordering: prompt first, then RAG, and only then consider fine-tuning — validate the gain with evaluation at every step.
    4. The two stack: fine-tuning provides base capability, prompts steer the current task.
  • Further reading: Prompt Engineering, Fine-Tuning and PEFT (LoRA), Building a RAG Application from Scratch

5. RAG ​

RAG is the most central knowledge module for applied AI roles — the pipeline, chunking, retrieval failures, and RAG vs. fine-tuning come up in almost every interview. Full engineering details in Building a RAG Application from Scratch.

1. Walk through the full RAG pipeline. ​

  • What's being tested: End-to-end system understanding (high frequency).
  • Key answer points:
    1. Offline: document cleaning → chunking → embedding → write to the vector store (optionally paired with an inverted index).
    2. Online: embed the query → retrieve top-k → (optional reranking) → assemble context → generate → post-processing/citations.
    3. Key design points: chunking strategy, embedding selection, retrieval and reranking, context assembly, faithfulness control.
    4. Bonus: pointing out the stages most often overlooked (e.g., permission filtering, document updates).
  • Further reading: Retrieval-Augmented Generation (RAG), Building a RAG Application from Scratch, Vector Databases and Semantic Search

2. How do you decide the chunking strategy, and why? ​

  • What's being tested: Engineering details that determine RAG retrieval quality.
  • Key answer points:
    1. Principle: preserve semantic completeness while controlling granularity — chunks too small lose context, chunks too large dilute relevance.
    2. Common strategies: fixed length with overlap, splitting by heading/paragraph structure, splitting at semantic boundaries.
    3. Evaluate the chunking scheme on your own data (retrieval hit rate + answer quality); don't copy someone else's parameters.
    4. Special documents (tables, code, scans) need to be converted into retrievable text structures first.
  • Further reading: Building a RAG Application from Scratch, Vector Databases and Semantic Search

3. What do you do when retrieval can't find the right document? ​

  • What's being tested: Debugging the pipeline and having a toolkit ready (high frequency).
  • Key answer points:
    1. Debugging layers: is the query itself clear → does the embedding match the domain's language → did chunking shred the key information → is top-k too small → is something wrong in the retrieval pipeline.
    2. Escalating remedies: hybrid retrieval (BM25 + vectors), reranking, HyDE (generate first, then retrieve), query rewriting/decomposition.
    3. Fallback: if retrieval finds nothing, say "I don't know" explicitly — never fabricate (faithfulness first).
    4. Last resorts: expand/update the knowledge base, fine-tune the embedding model for the domain.
  • Further reading: Building a RAG Application from Scratch, Vector Databases and Semantic Search, Perplexity and AI Search

4. What's the difference between RAG and fine-tuning? What does each solve? ​

  • What's being tested: Tool selection (guaranteed to be asked).
  • Key answer points:
    1. RAG: bolt-on knowledge that is updatable, traceable, and leaves the model untouched; solves "outdated/missing knowledge" and factuality problems.
    2. Fine-tuning: changes model behavior and expression (format, tone, domain terminology); solves "how to say it," not "what it is."
    3. Rule of thumb: knowledge problems → RAG; behavior problems → fine-tuning.
    4. The realistic combo: RAG supplies the facts, fine-tuning supplies the style, prompts do the orchestration.
  • Further reading: Retrieval-Augmented Generation (RAG), Fine-Tuning and PEFT (LoRA), Fine-Tuning Your Own LLM

5. How do you measure whether a RAG system is good? ​

  • What's being tested: Evaluation mindset (the point interviewers probe most).
  • Key answer points:
    1. Split it into two layers: retrieval (top-k hit rate, recall, MRR) and generation (faithfulness, relevance, completeness).
    2. Faithfulness is RAG's core metric: is the answer grounded in the retrieved content? Measure with LLM-as-judge or manual sampling.
    3. Build a fixed eval set (real questions + labeled answers) covering edge cases and failure scenarios.
    4. After launch, monitor business metrics: retrieval hit rate, user adoption rate, escalation-to-human rate.
  • Further reading: Building an LLM Evaluation Suite, LLM Evaluation and Benchmarks

6. The knowledge base got updated — how do you keep the vector store consistent? ​

  • What's being tested: Engineering maturity.
  • Key answer points:
    1. Incremental updates: new documents go through "chunk → embed → insert"; deletions/edits need synchronized handling (tombstone markers, version numbers).
    2. Consistency strategy: batch processing + version snapshots to avoid mixing "old vectors + new documents."
    3. Metadata and permissions: filter by source/department to control retrieval scope.
    4. For answers that cite outdated content: attach source version timestamps to the answers.
  • Further reading: Vector Databases and Semantic Search, Knowledge Graphs and Knowledge Injection, Common Pitfalls and Anti-Patterns

6. Agents ​

Agents are the module with the biggest year-over-year growth in 2025 interviews. The focus is on the ReAct paradigm, tool calling, memory, and safety. Full hands-on guide in Building an Agent from Scratch.

1. What is ReAct, and why does it work? ​

  • What's being tested: The core agent paradigm.
  • Key answer points:
    1. ReAct = Reasoning + Acting: alternate between emitting thoughts (reason) and actions (act, calling tools), observe the result, then keep reasoning.
    2. Compared with pure reasoning or pure action, ReAct lets the model revise its plan based on external feedback, reducing rigid execution.
    3. Implementation: define the tool list and output format in the prompt; the model runs a "Thought / Action / Observation" loop.
    4. Advanced: combining with CoT, token budget control for planning turns, loop limits.
  • Further reading: AI Agents, Building an Agent from Scratch, Manus and Agent Applications

2. How do you implement an agent's tool calling (function calling)? ​

  • What's being tested: Engineering implementation details.
  • Key answer points:
    1. Two paths: prompt-based (write the tool schemas into the prompt and have the model emit structured calls) and native function calling (the model is specifically trained to support it).
    2. Key pieces: clear tool descriptions, strict parameter schemas, tolerant output parsing, and feeding call results back into the context.
    3. Error handling: retries on tool failure, timeout control, permission checks.
    4. The more tools you have, the more you need tool-selection capability — front it with a router/classifier when necessary.
  • Further reading: Building an Agent from Scratch, Prompt Playbook

3. What kinds of memory does an agent have, and what does each solve? ​

  • What's being tested: Memory architecture.
  • Key answer points:
    1. Short-term memory: conversation and tool results within the context window (carried by the KV cache).
    2. Long-term memory: external storage (vector store / database / files) persisting user preferences, facts, and task state across sessions.
    3. Working memory: the running record of the current task (thoughts, intermediate results, plan lists).
    4. Engineering points: when to write memory, retrieval strategy, and privacy boundaries (what to keep, what to forget).
  • Further reading: AI Agents, Vector Databases and Semantic Search

4. What are the main security risks of agents, and how do you defend against them? ​

  • What's being tested: Security awareness (increasingly high frequency).
  • Key answer points:
    1. Prompt injection: the indirect kind — instructions hidden in tool-returned content manipulate the model.
    2. Privilege runaway: the agent mistakenly invokes high-privilege tools, loops endlessly, or burns through budget.
    3. Data leakage: user privacy written into memory, retrieval exceeding authorized scope.
    4. Mitigations: least privilege, approval for critical actions, timeouts and budget caps, output filtering, sandboxed execution.
  • Further reading: AI Safety and Governance, AI Agents, Building an Agent from Scratch

5. What does an agent add over plain RAG, and what does it cost? ​

  • What's being tested: Architectural trade-off judgment.
  • Key answer points:
    1. Gains: it can "do things" (call APIs, write code, query databases), plan and self-correct across multi-step tasks, and cover needs that one-shot RAG Q&A cannot.
    2. Costs: higher latency, larger token consumption, more failure modes (loops, hallucinated tool arguments, drifting off objective), and harder evaluation and debugging.
    3. Decision rule: use an agent when the task is decomposable and requires external actions; for pure knowledge Q&A, RAG is more reliable.
    4. Proactively saying "first decide whether you actually need an agent" earns credit.
  • Further reading: AI Agents, Common Pitfalls and Anti-Patterns, Manus and Agent Applications

6. An agent fails mid-task — how do you make it recoverable and observable? ​

  • What's being tested: Engineering maturity.
  • Key answer points:
    1. Observable: record full traces (thoughts, tool inputs/outputs) supporting replay to pinpoint failures.
    2. Recoverable: validation and retries on key steps, checkpointing to resume, graceful degradation on failure (e.g., escalate to a human).
    3. Constrained: caps on planning steps, tool allowlists, budget caps.
    4. Evaluable: measure across task success rate + cost + latency.
  • Further reading: Building an Agent from Scratch, Common Pitfalls and Anti-Patterns

7. Fine-Tuning and Alignment ​

This module tests PEFT principles and the evolution of alignment. LoRA, the three RLHF stages, and DPO vs. RLHF are the three guaranteed topics.

1. What is the principle behind LoRA, and why does it slash the number of trainable parameters? ​

  • What's being tested: PEFT principles (guaranteed to be asked).
  • Key answer points:
    1. Freeze the original weights W and inject a low-rank decomposition ΔW = B·A (A and B are low-rank matrices), training only A and B.
    2. Theoretical basis: weight changes during fine-tuning are typically low-rank (the intrinsic dimension hypothesis).
    3. Effect: trainable parameters drop to the 0.1%–1% range, memory and storage costs fall sharply, and results approach full fine-tuning.
    4. Practice: rank is a hyperparameter — too small lacks capacity, too large has diminishing returns; multiple LoRA adapters can be stacked/swapped.
  • Further reading: Fine-Tuning and PEFT (LoRA), Fine-Tuning Your Own LLM

2. What are the three stages of RLHF, and what does each one do? ​

  • What's being tested: The classic alignment paradigm (guaranteed to be asked).
  • Key answer points:
    1. SFT: supervised fine-tuning on instruction data so the model learns the format of "answering questions."
    2. RM: humans label preference pairs, then train a reward model to score outputs.
    3. RL: policy optimization with PPO or similar, using the reward model as the signal to align the policy model.
    4. Caveats: the reward model and the policy model need to be comparable in scale and distribution; PPO training is unstable and needs a KL constraint to prevent drift.
  • Further reading: Alignment: RLHF and DPO, Large Language Models (LLMs)

3. How does DPO differ from RLHF, and why is DPO simpler? ​

  • What's being tested: Understanding of frontier alignment (high frequency).
  • Key answer points:
    1. RLHF trains a reward model first and then runs RL optimization — a complex, unstable pipeline that needs massive online sampling.
    2. DPO derives a closed-form optimal policy directly from preference pairs, turning alignment into classification-style supervised learning with no reward model and no RL loop.
    3. Costs: it depends on high-quality preference data; without an explicit reward model, some scenarios (process rewards) are limited.
    4. Industry consensus: data quality and diversity often matter more than algorithm choice.
  • Further reading: Alignment: RLHF and DPO, DeepSeek-R1 and Reasoning Models

4. How do you choose between full fine-tuning and LoRA? ​

  • What's being tested: Engineering trade-offs.
  • Key answer points:
    1. Full fine-tuning: higher performance ceiling and more plasticity, but high memory/cost, prone to catastrophic forgetting, and deployment requires shipping the entire model.
    2. LoRA: saves memory, trains fast, supports swapping multiple adapters, makes A/B testing easy; results approach full fine-tuning in most scenarios.
    3. Decision rule: limited data, tight budget, frequent task switching → LoRA; chasing maximum performance with ample resources → full fine-tuning.
    4. Both need evaluation to validate; low-rank LoRA loses capability — it is not a silver bullet.
  • Further reading: Fine-Tuning and PEFT (LoRA), Fine-Tuning Your Own LLM

5. When should you fine-tune, and what should you try first? ​

  • What's being tested: Engineering decision order (high frequency).
  • Key answer points:
    1. Try first: prompt engineering, RAG, a stronger model — and use evaluation to confirm where the bottleneck actually is.
    2. Signals that fine-tuning is warranted: strict format/style consistency requirements, insufficient domain-term accuracy, sensitivity to token and latency costs, prompts that have become long and convoluted.
    3. What fine-tuning is for: injecting behavior and format, not new factual knowledge (knowledge comes from RAG/data updates).
    4. Mandatory after fine-tuning: compare against the baseline on the same eval set to guard against "it feels better."
  • Further reading: Fine-Tuning and PEFT (LoRA), Building an LLM Evaluation Suite, Retrieval-Augmented Generation (RAG)

6. What are reward hacking and over-alignment? How do you balance them? ​

  • What's being tested: A core problem in alignment research.
  • Key answer points:
    1. Reward hacking: the policy model exploits loopholes in the reward model — producing output that "looks pleasing" but is actually useless (sycophancy, empty filler, templated phrasing).
    2. Over-alignment: excessive compliance/excessive safety makes the model refuse tasks it should complete.
    3. Mitigations: iterate the reward model and policy from the same lineage, KL constraints, manual spot checks, diversity-aware evaluation.
    4. Conclusion: alignment is about balancing constraints — more alignment is not always better.
  • Further reading: Alignment: RLHF and DPO, AI Safety and Governance

8. Inference and Deployment ​

This module tests deployment fundamentals. The four high-frequency topics are the KV cache, quantization, performance metrics, and VRAM estimation. Hands-on details in Deploying LLMs and Optimizing Inference.

1. What is the KV cache, and why does it speed up generation? ​

  • What's being tested: Understanding inference mechanics (guaranteed to be asked).
  • Key answer points:
    1. Generation is autoregressive: each step computes only the new token's Q but attends over the K/V of all past tokens.
    2. Caching the historical K/V avoids recomputing at every step — that is the KV cache; the cost is memory growing linearly with sequence length.
    3. Optimization directions: GQA/MQA (shared KV heads), PagedAttention (paged management), KV quantization.
    4. Be able to estimate memory on the spot: roughly 2 × layers × KV heads × head dimension × bytes per element × sequence length.
  • Further reading: Inference Optimization and Quantization, Deploying LLMs and Optimizing Inference

2. What is the principle of quantization, what are the mainstream methods, and why might accuracy drop after quantizing? ​

  • What's being tested: Deployment fundamentals.
  • Key answer points:
    1. Principle: map FP16/BF16 weights (or activations) to lower precision (INT8/INT4), reducing memory and bandwidth in exchange for higher throughput.
    2. Methods: post-training quantization (GPTQ, AWQ) and quantization-aware training (related to QLoRA); granularity ranges from per-tensor to per-channel.
    3. Why accuracy drops: low precision introduces rounding error, and the loss is pronounced when activations have large outliers (AWQ was designed precisely to protect important channels).
    4. Practice discipline: always run the eval set after quantization — never judge by the memory number alone.
  • Further reading: Inference Optimization and Quantization, Deploying LLMs and Optimizing Inference

3. What are the key performance metrics for deploying an LLM? ​

  • What's being tested: Awareness of engineering metrics.
  • Key answer points:
    1. TTFT (time to first token): first-token latency, which determines the interactive feel.
    2. TPOT / TPS (time per output token / tokens per second): generation speed.
    3. Throughput (requests/s or tokens/s) and concurrency capacity.
    4. Also worth adding: P99 latency, memory footprint, and how batch size and continuous batching affect throughput.
  • Further reading: Deploying LLMs and Optimizing Inference, Inference Optimization and Quantization

4. Roughly how much GPU memory does a 7B model need for FP16 inference? ​

  • What's being tested: Memory estimation (quick-fire round).
  • Key answer points:
    1. Weights: 7B × 2 bytes ≈ 14GB.
    2. Adding the KV cache and activations, real deployments typically need 20GB+; after INT4 quantization the weights are about 3.5GB, which runs on consumer GPUs.
    3. Memorize the rule of thumb: FP16 = 2 bytes per parameter, INT8 ≈ 1 byte, INT4 ≈ 0.5 bytes.
    4. If memory falls short: quantize, lower concurrency, or use model parallelism/tensor parallelism.
  • Further reading: Deploying LLMs and Optimizing Inference, Inference Optimization and Quantization

5. What are continuous batching and speculative decoding? ​

  • What's being tested: Frontier inference optimization.
  • Key answer points:
    1. Continuous batching: requests join and leave the batch at token granularity instead of "whole-request batches that wait for each other," dramatically improving GPU utilization (a core feature of vLLM and similar engines).
    2. Speculative decoding: a small model drafts several candidate tokens, and the large model verifies them in one pass — the output distribution is unchanged but generation speeds up.
    3. At bottom, both are engineering trade-offs of "compute for latency" or "latency for throughput."
    4. Bonus: noting that they address the "memory fragmentation + compute fragmentation" problems.
  • Further reading: Inference Optimization and Quantization, Deploying LLMs and Optimizing Inference

9. Evaluation ​

Evaluation thinking is the hidden bonus round of AI-role interviews — a good answer directly proves you have actually built systems. The full methodology is in Building an LLM Evaluation Suite.

1. How do you evaluate LLMs, and what are the mainstream benchmarks? ​

  • What's being tested: Understanding of the evaluation landscape.
  • Key answer points:
    1. Layered: foundational capability benchmarks (MMLU for knowledge, GSM8K for math, HumanEval for code) → domain/task evals → business-line evals.
    2. Limits of benchmarks: data contamination, metrics divorced from real user experience, and a single number masking the structure of capabilities.
    3. Engineering evaluation: real task samples + labeled answers + multi-dimensional metrics (accuracy/faithfulness/relevance).
    4. Worth adding the view that "leaderboard scores are just the starting point — business metrics are what count."
  • Further reading: LLM Evaluation and Benchmarks, Building an LLM Evaluation Suite, Models and Leaderboards Cheat Sheet

2. Is LLM-as-judge (using an LLM as the grader) reliable? ​

  • What's being tested: Evaluation methodology (a frequent follow-up).
  • Key answer points:
    1. Strengths: cheap and scalable, with high correlation to human judgments (papers report correlations around 0.8).
    2. Risks: judges have their own biases (length bias, position bias, self-preference); they are insensitive to domain factual errors.
    3. Mitigations: use a strong model as the judge, structured scoring + rationales, consistency checks against manual sampling, multi-judge voting.
    4. Conclusion: LLM-as-judge is a cost-effective tool, not the truth — important conclusions need human review.
  • Further reading: Building an LLM Evaluation Suite, LLM Evaluation and Benchmarks

3. What is test set contamination, and how do you prevent it? ​

  • What's being tested: Evaluation credibility.
  • Key answer points:
    1. Contamination: pretraining data includes public benchmark questions, so the model "memorizes the exam" instead of "knowing the material," inflating metrics.
    2. Symptom: leaderboard scores diverge from real-task performance.
    3. Prevention: build your own private eval set; detect overlap with training data via n-gram matching; watch for anomalous swings in benchmark scores.
    4. Engineering implication: run a contamination check before reporting metrics externally.
  • Further reading: LLM Evaluation and Benchmarks, Building an LLM Evaluation Suite

4. How do offline and online evaluation differ, and what pitfalls does each guard against? ​

  • What's being tested: The evaluation loop.
  • Key answer points:
    1. Offline: score against a fixed eval set — fast and reproducible, but it cannot cover the real traffic distribution.
    2. Online: A/B experiments + behavioral metrics (adoption rate, escalation rate, retention) — real, but slow and noisy.
    3. The right approach: screen offline → validate online, letting the two layers corroborate each other.
    4. Common pitfall: when offline and online metrics disagree, first check evaluation definitions and sample distributions.
  • Further reading: Building an LLM Evaluation Suite, LLM Evaluation and Benchmarks

5. How do you define and measure faithfulness, relevance, and completeness? ​

  • What's being tested: A metrics system for generation quality.
  • Key answer points:
    1. Faithfulness: whether every claim in the answer is grounded in the retrieved material/knowledge source (RAG's core metric).
    2. Relevance: whether the answer addresses the user's question — no drifting off topic, no answering a different question.
    3. Completeness: whether the answer covers all the key points without omitting critical information.
    4. Measurement: human labeling + LLM-as-judge scoring per dimension; when dimensions conflict, faithfulness wins.
  • Further reading: Building an LLM Evaluation Suite, Retrieval-Augmented Generation (RAG)

10. End-to-End Scenario Questions ​

Scenario questions have no standard answer — you are scored on your framework. Here is a universal skeleton: requirements analysis → architecture design → evaluation system → launch and iteration → trade-off rationale.

1. Given a requirement for "internal enterprise knowledge base Q&A," how would you design the whole system? ​

  • What's being tested: End-to-end system design (the centerpiece — a question that threads the entire documentation chain together).
  • Key answer points:
    1. Requirements analysis: who the users are (employees/support staff/customers), what they ask about (policies/processes/products), the cost of a wrong answer, data shapes (documents/spreadsheets/system APIs), and permission requirements.
    2. Architecture design: RAG-centric — document cleaning and chunking → embedding + metadata → hybrid retrieval (vectors + keywords) → reranking → generation (faithfulness constraints + citations) → permission filtering.
    3. Evaluation system: build a domain eval set (real questions + labeled answers); metrics = retrieval hit rate + answer faithfulness/relevance + manual spot checks; offline first, then A/B.
    4. Launch and iteration: monitoring (retrieval hit rate, escalation-to-human rate, user feedback), an incremental knowledge base update process, rollback on failure.
    5. Trade-off rationale: why RAG instead of fine-tuning (knowledge updates often, needs traceability); why hybrid retrieval is needed (long-tail proper nouns).
  • Further reading: Building a RAG Application from Scratch, Retrieval-Augmented Generation (RAG), Building an LLM Evaluation Suite, Common Pitfalls and Anti-Patterns

2. In a customer support scenario where "a wrong answer is worse than no answer," how do you control hallucination and set fallback strategies? ​

  • What's being tested: Risk thinking + metric design.
  • Key answer points:
    1. Faithfulness first: generation must be grounded in retrieved material; when there is no supporting evidence, escalate to a human explicitly.
    2. Layered fallbacks: retrieval hit thresholds, sensitive-topic allowlists, auto-answer for low risk / human handoff for high risk.
    3. Dual-channel design: simple high-frequency questions go to the LLM; complex or high-complaint-risk questions go to humans.
    4. Evaluation and monitoring: a dedicated hallucination eval set, escalation and complaint-rate metrics, staged rollout.
    5. Security and compliance: permission controls, audit logs, content filtering.
  • Further reading: AI Safety and Governance, Building an LLM Evaluation Suite, Common Pitfalls and Anti-Patterns

11. Extra Interview Tactics ​

1. Live coding: likelihood and prep scope ​

The odds of live coding in an applied LLM role interview are lower than in traditional algorithm roles, but walking in completely unprepared will bite you. The common combo is "one general algorithm question + one LLM-flavored implementation task":

  • High-frequency general algorithm topics: arrays/hashing, two pointers, sliding window, binary search, simple DP, linked list/tree traversal.
  • LLM-flavored implementation tasks: a minimal self-attention, an LLM-as-judge scoring function, a top-k retrieval.
  • Practice discipline: get a brute-force solution running first, then optimize the complexity; state time/space complexity clearly for every problem — the most commonly overlooked hidden scoring criterion. For coding habits on the engineering side, see Common Pitfalls and Anti-Patterns.

2. Project deep-dives: the killer question is "how did you evaluate your RAG?" ​

The project round has the highest elimination rate of any AI-role round. For every project on your resume, prepare answers to three layers of follow-ups: the numbers → how they were measured → why you made each decision. The three most frequent follow-ups:

  • "How did you evaluate your RAG?" — Answer: how many cases are in the eval set, which metrics (retrieval hit rate + faithfulness + relevance), why those metrics, the level of human-vs-automated agreement, and how you analyzed failure cases. Methodology in Building an LLM Evaluation Suite.
  • "Why this vector database / this model?" — Answer: which alternatives you compared, what criteria drove the choice, and what trade-offs it carried.
  • "What was your biggest failure?" — Answer: the pitfall → how you discovered it → how you fixed it → what you took away from it. Never having hit a pitfall is itself a red flag.

Answering principle: every number needs a definition, every choice needs a reason, every pitfall needs a retrospective — the interview-room continuation of the "context-action-result" formula from Resume Polishing.

3. Reverse questions: the hidden bonus round ​

Prepare 2–3 high-quality reverse questions that show your evaluation mindset and problem orientation:

  • "How does your team evaluate model performance in production?" (shows evaluation thinking)
  • "Where is the biggest technical bottleneck right now?" (shows problem orientation)
  • "What is the most important deliverable for this role in the first six months?" (shows delivery awareness)

Avoid questions whose answers are on the company website or the job posting. The quality of your reverse questions is the interviewer's final test of whether you truly know this field.

Three iron rules for the interview room

① Conclusion first, then elaborate: nail the main point in 30 seconds, then go deeper as asked. ② If you don't know, say so and state the boundary of what you do understand — never fabricate; getting caught bluffing is far worse than admitting "I don't know." ③ End every answer with a one-line conclusion, making it easy for the interviewer to remember and score.

Further Reading ​

Keep reading on this site:

References ​

One last reminder: a question bank is a map, not the destination. What actually gets you through interviews will always be the projects on your resume that you built with your own hands and can talk about for 20 minutes — the question bank turns knowledge points into reflexes, and your projects turn those reflexes into credible evidence.