laoshan-song

laoshan-song / Awesome-LLM-Interview

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LLM interview prep notes: Transformer, RLHF, DPO, LoRA, KV Cache,RAG, MoE, distributed training & 2026 frontier topics

38
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89% credibility
Found May 29, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Summary

A Chinese-language study resource that helps people prepare for job interviews focused on Large Language Models, containing 29+ organized topics with explanations, research paper links, video tutorials, and a quick-review webpage with 49 practice questions.

How It Works

1
🔍 You discover a study guide for LLM interviews

While preparing for a job interview at a tech company, you find an online collection that organizes everything you need to know about AI language models.

2
📚 You browse through organized topics

The guide is neatly split into sections like core concepts, training methods, speed improvements, and the latest research breakthroughs—everything is in one place.

3
🎯 You focus on topics you find tricky

Stuck on how position encoding works or what MoE means? Each section has clear explanations, original research papers, and video tutorials to help you understand.

4
📱 You open the quick-review webpage

The cheatsheet page has 49 common interview questions across 7 categories—you can search, filter, and reveal answers with one click.

5
You choose your learning path
📖
Study the detailed notes

Go deep into each topic with diagrams, papers, and videos before testing yourself.

⚡
Practice with the cheatsheet

Start answering questions right away and look up explanations only when you get stuck.

💪 You feel confident and ready

After reviewing the topics, watching the videos, and testing yourself, you walk into your interview feeling prepared and self-assured.

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AI-Generated Review

What is Awesome-LLM-Interview?

A Chinese-language LLM interview prep repository that organizes core concepts like Transformer architecture, RLHF, DPO, LoRA, KV Cache, RAG, MoE, and distributed training into structured notes. It includes a searchable cheatsheet with 49 high-frequency interview questions, links to original papers, video tutorials, and a companion project library for hands-on practice. The content is organized into five modules covering fundamentals, training alignment, inference optimization, distributed training, and frontier topics including 2026 trends.

Why is it gaining traction?

The searchable cheatsheet interface lets you filter and expand answers in 30 minutes before an interview. Each topic links to source papers and video explanations rather than just summarizing concepts. The project also maintains a curated list of GitHub projects and Kaggle datasets organized by theme, making it practical for building portfolio pieces. Active maintenance shows recent additions covering DeepSeek-R1, GraphRAG, and MCP protocols.

Who should use this?

Job seekers preparing for LLM engineer or research scientist roles at AI companies. Developers transitioning from traditional ML to LLMs who need structured learning paths. Technical interviewers looking for a topic checklist might find it useful as well.

Verdict

At 38 stars, this is a young project with limited community validation. The credibility score of 0.9% reflects that low traction. However, the content organization is practical and the cheatsheet format fills a real gap for interview prep. Worth bookmarking as a supplementary resource while the community grows.

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