Generative AI Concepts: How LLMs Work, Why They Fail, and How to Fix Problems
Summary : A clear post about the core concepts behind generative AI - emergent abilities, chain-of-thought, hallucinations and RAG, human-alignment via RLHF, and foundation models. Practical examples and tips for using these ideas responsibly and effectively. Introduction Generative AI tools like ChatGPT feel effortless: you type, they answer. That ease hides a complex stack of engineering and surprising mechanics. Understanding how these models work helps you get better results, spot their limits, and use them safely. View the Generative AI Builder's Journey first. Next, this post explains five essential concepts that drive generative AI today and what they mean for everyday users and builders. 1. Bigger Is Not Just Better - It Can Be Unpredictably Different In many systems, adding scale produces steady improvement. With large language models (LLMs), scale sometimes unlocks new, unexpected skills called emergent abilities. A small model might fail entirely at a task, while...