aceliuchanghong/FAQ_Of_LLM_Interview

What it solves

This project provides a curated collection of interview questions and conceptual explanations for individuals preparing for Large Language Model (LLM) algorithm engineering roles. It helps candidates navigate the broad and rapidly evolving landscape of AI technical interviews.

How it works

The repository acts as a study guide, organizing essential knowledge into key pillars: mathematical and programming foundations, model architectures (such as Transformers and MoE), document processing (RAG), reinforcement learning, and agent systems. It directs users to specific topics and external resources to build a comprehensive understanding of the field.

Who it’s for

It is designed for job seekers targeting algorithm-focused roles in the LLM industry who need a structured overview of the technical concepts and common interview topics they are likely to encounter.

Highlights

• Covers core mathematical requirements including linear algebra, calculus, and statistics.
• Explains critical model architectures like Transformer variants, diffusion models, and Mixture of Experts (MoE).
• Includes practical guidance on RAG (Retrieval-Augmented Generation) and document processing pipelines.
• Provides a roadmap for reinforcement learning concepts, including PPO and DPO, as applied to LLMs.
• Offers insights into modern agent system development and evaluation methodologies.