Fourth Industrial Revolution

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...

In this post, I explain LLMs in Python, Python Setup & Installation, Inference with Transformers, Calling ChatGPT API in Python, Python Local Deployment with Hugging Face Models, Prompt Engineering in Python and FineTuning & Custom Training (including LoRA). You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers. If you want my complete Introduction to LLMs in Python document that additionally includes the following important topics, you can message me on LinkedIn : Python Advanced Techniques (Streaming, Batching & Callbacks), Python Efficiency & #Optimization (quantization, distillation, and parameter‑efficient tuning), Integration & Deployment Workflows, LLMs in Python Best Practices & Troubleshooting, and consolidated Introduction to LLMs in Python Quiz (with answer explanations to reinforce learning). Question : What do I mean by "Introduction to LLMs in Python"? Answer : Introduction to LL...

In this post, I explain Introduction to Prompt Engineering for ChatGPT, Key Concepts and Prompt Types (such as zero-shot, few-shot, chain-of-thought prompting), Best Practices, Advanced Prompt Engineering Tactics, Prompt Engineering for Coding and Testing, Multi‑modal and Complex Prompts and Evaluating and Iterating Prompts. You can test your knowledge of Prompt Engineering by attempting the Quiz after every set of Questions and Answers. If you want my complete Prompt Engineering for ChatGPT document that additionally includes the following important topics, you can message me on LinkedIn : Prompt Engineering Tools and Frameworks (GitHub repositories, APIs), Ethics and Prompt Safety, Use Cases and Workflows and Interview Preparation and Prompt Engineering Quiz. Question : What is prompt engineering for ChatGPT? Answer : Prompt engineering for ChatGPT is the deliberate design and structuring of input text to guide the model’s behavior toward desired outputs. By crafting precise...