🚀 Master Key LLM Interview Concepts with One Book!
This book compiles the most cutting-edge and high-frequency LLM (Large Language Model) interview questions in 2025, covering fundamental concepts, pretraining and fine-tuning, inference optimization, Retrieval-Augmented Generation (RAG), reinforcement learning (RLHF), AI agents, engineering practices, and future trends.
Designed for AI researchers, ML engineers, data scientists, and job seekers preparing for LLM-related technical interviews, this book helps you stay ahead of the competition while understanding the latest advancements in LLMs.
🔹 Key Topics Covered:
✅ LLM Fundamentals – Transformer, LayerNorm, FlashAttention, MoE, and more
✅ Pretraining & Fine-Tuning – Fine-tuning, LoRA, Adapter Tuning, incremental pretraining
✅ RAG (Retrieval-Augmented Generation) – Optimizing external knowledge retrieval, reducing hallucinations
✅ Inference Optimization – vLLM, DeepSpeed, PagedAttention, distributed inference techniques
✅ Reinforcement Learning – RLHF, GRPO, DPO, PPO applications in LLM training
✅ LLM Agents – Task execution, multi-turn conversations, memory management, tool usage
✅ Engineering Best Practices – Handling Prompt Injection, data contamination, improving generation quality
✅ Future Trends – Compute bottlenecks, MoE vs. Dense Transformer, LLM + Quantum Computing
Whether you are aiming to excel in big tech interviews or dive deep into the future of LLMs and AI, this book is your essential guide! 📖