Foundation Models Explained: Transformers, Scaling Laws & RLHF | Chapter 2

About this lesson

Download the source code from here: https://onepagecode.substack.com/ In this chapter, we go deep into how foundation models actually work — from the famous Transformer architecture to why bigger models need more data (Scaling Laws), and how post-training (SFT + RLHF) makes models usable. This is one of the most important chapters if you want to truly understand modern AI systems like GPT, Claude, Llama, and Gemini. What you’ll learn in this video: • Why training data quality and distribution matter so much • Multilingual and domain-specific models • The Transformer architecture and the Attention mechanism (explained simply) • Model size, parameters, and the Chinchilla Scaling Law • Pre-training vs Post-training (Supervised Finetuning + Preference Tuning) • RLHF and Reward Models explained • Sampling strategies: Temperature, Top-k, Top-p • Why LLMs hallucinate and behave inconsistently • Structured outputs and test-time compute This chapter builds the technical foundation needed to understand model selection, evaluation, and adaptation in later chapters. If you're preparing for AI engineering interviews, building LLM applications, or just want a clear technical understanding of how these models work under the hood, this video is for you. Drop a comment: Which part of foundation models confuses you the most — Attention, Scaling Laws, RLHF, or Hallucinations? #FoundationModels #TransformerArchitecture #LLM #ScalingLaws #RLHF

Original Description