For students, researchers, and the curious who want to follow the research and the frontier. This journey is a living feed: revived monthly-paper lists, research tables kept current, and the annual State of AI report.
- Understand 101 🟢: foundations and how to read research
- Understand 201 🟡: active research areas
- Understand 301 🔴: the frontier feed
How to read a row: title, then tags. Level 🟢/🟡/🔴 · Format 📖 Read / 🎥 Video / 🛠️ Notebook / 📝 Practice · Source ⭐ LevelUp Labs original / 🌐 External · year.
Related: browse all topics · sibling journeys Use AI and Build AI.
Transformers, pretraining, seminal papers, and how to read a paper.
- Survey Papers ⭐ 📖 (2024) 🔴 aging: a curated set of survey papers across LLM topics.
- LLM Lingo ⭐ 📖: a 6-part glossary of commonly used LLM terms with plain-language definitions. Topic: Foundations. Also relevant to Build 101.
- Agentic AI Crash Course ⭐ 📖 (2025): a conceptual grounding in agents that also serves builders. Primary home: Build 101.
- Generative AI Genius ⭐ 📖 (2024): a beginner introduction. Primary home: Use 101.
The foundations courses below are the primary starting point for understanding LLMs, and they also serve builders working through Build 101. Topic: Foundations.
- Large Language Models 🌐 📖 by ETH Zurich
- Understanding Large Language Models 🌐 📖 by Princeton
- Transformers course 🌐 📖 by Huggingface
- NLP course 🌐 📖 by Huggingface
- CS324 - Large Language Models 🌐 📖 by Stanford
- Generative AI with Large Language Models 🌐 📖 by Coursera
- Introduction to Generative AI 🌐 📖 by Coursera
- Generative AI Fundamentals 🌐 📖 by Google Cloud
- 5-Day Gen AI Intensive Course 🌐 🎥 by Google and Kaggle
- Introduction to Large Language Models 🌐 📖 by Google Cloud
- Introduction to Generative AI 🌐 📖 by Google Cloud
- 1 Hour Introduction to LLM (Large Language Models) 🌐 🎥 by WeCloudData
- LLM Foundation Models from the Ground Up: Primer 🌐 🎥 by Databricks
- Generative AI Explained 🌐 📖 by Nvidia
- Transformer Models and BERT Model 🌐 📖 by Google Cloud
- Generative AI Learning Plan for Decision Makers 🌐 📖 by AWS
- Introduction to Responsible AI 🌐 📖 by Google Cloud
- Fundamentals of Generative AI 🌐 📖 by Microsoft Azure
- Generative AI for Beginners 🌐 📖 by Microsoft
- [1hr Talk] Intro to Large Language Models 🌐 🎥 by Andrej Karpathy
- ChatGPT for Everyone 🌐 📖 by Learn Prompting
- Large Language Models (LLMs) (In English) 🌐 🎥 by Kshitiz Verma (JK Lakshmipat University)
Latest additions (2025-2026):
- 🆕 Neural Networks: Zero to Hero 🌐 🎥 (2025) by Andrej Karpathy: build a GPT from scratch in code.
- 🆕 CS336: Language Modeling from Scratch 🌐 📖 (2025) by Stanford: the full LLM pipeline.
- 🆕 How Transformer LLMs Work 🌐 🎥 (2025) by DeepLearning.AI (Jay Alammar): visual intuition for transformer internals.
- 🆕 Pretraining LLMs 🌐 🎥 (2025) by DeepLearning.AI and Upstage: pretraining end to end, from data to eval.
These 2 courses touch responsible use and governance, relevant here and on the Safety and Security topic page.
- Avoiding AI Harm 🌐 📖 by Coursera
- Developing AI Policy 🌐 📖 by Coursera
Next: Understand 201.
The living research tables track the active areas: reasoning, agents, RAG, alignment, and evaluation.
- RAG Research Table ⭐ 📖 (2026): landmark and recent RAG papers with descriptions and tags. Topic: RAG. Also informs Build 301.
- AI Evaluation 2025 Table ⭐ 📖 (2025): evaluation research organized by category. Topic: Evaluation. Also informs Build 301.
- Agentic Search and Retrieval Table ⭐ 📖 (2026): agentic search and retrieval research. Topics: Agents, RAG. Also informs Build 301.
Next: Understand 301.
Monthly papers, the annual report, and open problems.
- State of AI 2025 Report ⭐ 📖 (2026): a full report with its own build pipeline (HTML and PDF).
- Monthly Best Papers: 2026 ⭐ 📖 (2026): the current monthly feed, January through June 2026.
- Monthly Best Papers: 2025 ⭐ 📖 (2025): the full year, January through December 2025.
- Monthly Best Papers: 2024 (archive) ⭐ 📖 🔴 archived: 11 monthly lists from 2024, kept for reference.
The monthly feed is updated regularly with each month's most notable applied AI papers.
Sibling journeys: Use AI · Build AI. Back to the repository index.