Fourth Industrial Revolution

Summary : Learn five practical prompting techniques—role prompting, few-shot examples, chain-of-thought, guardrails, and self-consistency—that help you get precise, reliable, and safer outputs from AI tools like ChatGPT. Use these methods to turn generic answers into expert-level responses. Why prompts matter Great AI output starts with a great prompt. If your results feel generic or off-target, the issue is rarely the model; it is the input. Prompt engineering is the practical skill of crafting inputs so the model reliably produces the outcome you want. These five techniques will help you move from reactive questioning to deliberate instruction. View my  Prompt Engineering & Prompting Techniques video . Then read on. 1. Give the AI a job title: Role Prompting Assign a persona or role to the model to frame its tone, vocabulary, and depth. For instance, asking an AI to "act as a cybersecurity expert" leads to technical, risk-focused answers. Role prompting is a quic...

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...

Summary : Five surprising truths about how AI language models really work — from tokens and sudden, scale-driven abilities to why they sometimes "hallucinate", how you can program them with plain language, and how retrieval systems make them more reliable. Introduction If you've used tools like ChatGPT, you know how effortlessly they can write an email, generate code, or explain a concept. That ease feels close to magic. Under the surface, however, these systems run on patterns, probabilities, and careful engineering. Understanding a few core ideas will help you use them smarter and more safely. View my  LLM Concepts video below and then read on. 1. They Don’t See Words, They See Tokens When you type a sentence, you see words and spaces. A large language model (LLM) processes a sequence of tokens. Tokens are the smallest pieces the model works with — sometimes a whole word, sometimes a subword fragment. For example, “unbelievable” might be broken into subword parts...