语言模型面试手册
151 Interview Questions, Foundation Roadmaps, Python Examples,Architecture Diagrams, and Production Playbooks for Modern LLM andGenAI Roles Lamhot Siagian AI Engineering Insider Copyright Copyright©2026 Lamhot Siagian.Imprint: AI Engineering Insider. All rights reserved. This handbook is intended for educational and professional interview-preparation use. It iswritten as a compact technical reference for engineers, researchers, students, and practitionersworking with large language models and retrieval-centered AI systems. Preface Large language models are often introduced either as intimidating research artifacts or asmagic productivity tools. Neither framing helps much in a real interview. Hiring panels wantcandidates who can explain how tokenization, attention, retrieval, prompting, fine-tuning, anddeployment actually work together under production constraints. This handbook was revised tomeet that need directly. The book is now organized across sixteen chapters and one hundred fifty-one interview questions,with a stronger emphasis on foundations, career roadmap framing, architecture diagrams,premium chapter summaries, code walkthroughs, and interview positioning. The new openingchapter establishes what an LLM is, how the field is evolving, how to sequence your learning,and how to position yourself for GenAI roles. The next chapters build the technical foundations:tokens, embeddings, attention, pretraining, and model families.Middle chapters move intoclassification, theme discovery, retrieval, RAG, and prompting. Later chapters cover multimodalsystems, embedding optimization, PEFT, training math, decoding, serving, and productiondeployment. Each chapter now includes two deliberate interview aids.Interview Anchorsections explainwhat a strong candidate should emphasize when answering aloud.INTERVIEW CHEAT-SHEETpanels convert that into compact talking points, trade-offs, and red flags that are easyto review before a screen, onsite, or take-home discussion. The goal of this handbook is not memorization for its own sake. The stronger goal is to helpyou sound like an engineer who can reason from first principles, choose the right tool for theworkload, articulate failure modes, and justify trade-offs with clarity. That is the differencebetween reciting terminology and demonstrating real technical judgment. Contents Preface iii 2.1What is a token and why is it the real unit of computation in an LLM?. . . . . . . . . . . . . .102.2Why do tokens not map cleanly to words?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112.3How does byte-pair encoding help modern language models?. . . . . . . . . . . . . . . . . . . . . .112.4What is SentencePiece and when is it preferable to classic whitespace-based.... . . . . . . . .122.5What is a context window?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .122.6Why does tokenization directly affect cost and latency?. . . . . . . . . . . . . . . . . . . . . . . . . . .132.7What happens when an input is longer than the model can accept?. . . . . . . . . . . . . . . . .132.8What is the difference between truncation, sliding windows, and summarization?. . . . . .142.9Why are special tokens important in model behavior?. . . . . . . . . . . . . . . . . . . . . . . . . . . .142.10How should engineers budget tokens in a production LLM system?. . . . . . . . . . . . . . . . .15 3Embeddings and Semantic Representations16 3.1What is an embedding?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .183.2Why do embeddings make semantic search possible?. . . . . . . . . . . . . . . . . . . . . . . . . . . . .183.3What is the difference between token embeddings, sentence embeddings, and.... . . . . . . .193.4Why do engineers often L2-normalize embeddings?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .193.5When should you use cosine similarity instead of dot product?. . . . . . . . . . . . . . . . . . . . .203.6What are hubness and anisotropy in embedding spaces?. . . . . . . . . . . . . . . . . . . . . . . . . .203.7What is the difference between dense and sparse representations?. . . . . . . . . . . . . . . . . . .213.8What is the difference between a bi-encoder and a cross-encoder?. . . . . . . . . . . . . . . . . . .213.9How does embedding dimension affect system design?. . . . . . . . . . . . . . . . . . . . . . . . . . . .223.10How do you evaluate an embedding model before using it in production?. . . . . . . . . . . . .22 4Transformer Architecture, Attention, and Positional Reasoning 4.1Why was the transformer such a major breakthrough?. . . . . . . . . . . . . . . . . . . . . . . . . . .254.2What is self-attention in simple terms?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .254.3What roles do query, key, and value vectors play in attention?. . . . . . . . . . . . . . . . . . . . .264.4Why do transfo
