This comprehensive masterclass by Andrej Karpathy provides a deep and intuitive explanation of how Large Language Models (LLMs) like ChatGPT are built, trained, and used in real-world applications. It is designed for learners who want both technical depth and conceptual clarity.

The course begins by explaining pretraining data sources such as the internet and how raw text is transformed into tokens through tokenization. It then explores how neural networks process inputs and generate outputs during inference, helping learners understand the internal mechanics of LLMs.

You will learn how models like GPT-2 and Llama 3.1 are trained and how the full pipeline transitions from pretraining to post-training using conversational datasets. The course also explains important concepts such as hallucinations, tool usage, and the limitations of model memory and reasoning.

Advanced topics include supervised fine-tuning (SFT), reinforcement learning, and reinforcement learning from human feedback (RLHF), which are key techniques used to align models with human preferences. The course also discusses DeepSeek-R1, AlphaGo, and modern AI reasoning systems.

In addition, learners will gain insight into token limitations, model behavior (“jagged intelligence”), and how LLMs “think” using tokens. The course concludes with a high-level summary of the current AI landscape and future directions.

By the end, learners will have a complete mental model of how ChatGPT-style systems work from data collection to advanced alignment techniques.