Summary

This repository is a comprehensive guide for preparing for interviews related to Large Language Models (LLMs) and AI Agents. It targets engineers, researchers, and power users seeking to deepen their understanding of core concepts and practical applications in the LLM/Agent domain, offering a structured learning path and practical coding examples to enhance interview readiness.

How It Works

The project curates a vast collection of interview-style questions ("八股文") across nine key modules, ranging from foundational Transformer architecture and inference optimization to advanced topics like RAG, Agent frameworks, and system design. It provides detailed explanations, links to seminal papers, and over ten hands-on coding implementations of critical algorithms (e.g., Self-Attention, LoRA, Beam Search), enabling users to grasp theoretical concepts and practical coding challenges.

Quick Start & Requirements

This repository serves as a knowledge base and study guide, not a runnable software project. It does not require installation or specific runtime environments. Users can directly access and study the content. Links to recommended papers and external resources are provided for deeper dives.

Highlighted Details

• Covers over 300 interview questions, including 11 real interview experiences from major tech companies like ByteDance.
• Features hands-on coding implementations for more than 10 fundamental LLM algorithms and techniques.
• Organized into a structured learning path, progressing from basic knowledge to advanced Agent systems and system design.
• Includes coverage of cutting-edge trends and recent advancements in LLMs and Agents for 2025-2026.

Maintenance & Community

The project is marked as "continuously updated" and actively encourages community contributions through Issues and Pull Requests. It provides links to related open-source interview resources.

Licensing & Compatibility

The project is licensed under the Apache License 2.0, which permits commercial use and modification, provided attribution and license terms are followed.

Limitations & Caveats

As a curated guide, this repository does not provide executable code for building LLM agents or systems. Its primary focus is on interview preparation, and while it covers many topics, it may not delve into every niche aspect of LLM/Agent development or deployment. The depth of coverage for each topic can vary.