Fourth Industrial Revolution

Summary : This is practical advice for building reliable LLM applications in Python. Learn secure secret handling, few-shot prompting, efficient fine-tuning (LoRA), quantization for local inference, and strategies to manage the model context window. First, view the 7-minute Intro to LLMs in Python video for explanations. Then read on. 1. Treat API keys like real secrets Never hard-code API keys in source files. Store keys in environment variables and load them at runtime. That keeps credentials out of your repository and reduces the risk of accidental leaks. Example commands: export OPENAI_API_KEY="your_key_here" # Linux / macOS set OPENAI_API_KEY="your_key_here" # Windows (Command Prompt) For production, use a secure secrets manager (Azure Key Vault, HashiCorp Vault) and avoid committing any credential material to version control. 2. Guide models without heavy fine-tuning: few-shot prompting You can shape an LLM's behavior by giving it examples i...

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...

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...