Large Language Models Explained: How LLMs Work
Large language models (LLMs) explained in under 10 minutes: how ChatGPT, Claude and Gemini actually work.Tokens, embeddings, transformers and attention, next-token prediction and temperature, pre-training and scaling laws,
fine-tuning and RLHF, context windows and RAG, reasoning models, tools and AI agents, plus hallucinations and how to use LLMs well.
▶ Statistics for Data Science Full Course: https://www.youtube.com/watch?v=GGxxMm7KsZM
Chapters
0:00 What is an LLM?
0:30 The one big idea
1:03 Tokens
1:46 Embeddings
2:21 Transformers & attention
3:04 Layers & parameters
3:41 Probabilities & temperature
4:19 Pre-training
5:13 Fine-tuning & RLHF
6:00 Context window & RAG
6:49 Reasoning, tools & agents
7:36 What LLMs get wrong
8:27 How to use them well
8:57 Recap
Sources & further reading
- Vaswani et al., "Attention Is All You Need" (2017): https://arxiv.org/abs/1706.03762
- Hoffmann et al., "Training Compute-Optimal Large Language Models" (Chinchilla, 2022): https://arxiv.org/abs/2203.15556
- Ouyang et al., "Training language models to follow instructions with human feedback" (2022): https://arxiv.org/abs/2203.02155
- Kalai et al., "Why Language Models Hallucinate" (2025): https://openai.com/index/why-language-models-hallucinate/
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