Fourth Industrial Revolution

Summary : Context Engineering is the architecture that lets AI remember, personalize, and act reliably across sessions. Beyond crafting clever prompts, it assembles the right data, tools, and memory hygiene so AI systems behave like thoughtful personal assistants,  and not forgetful librarians. Beyond RAG: Why Most AI Forgets the Moment You Close the Chat We’ve all had the same experience: a helpful conversation with an AI assistant, then a fresh chat that treats us like a total stranger. Every interaction feels like the first. That friction isn’t just annoying, but it also exposes a core architectural limitation of many AI systems. By default, Large Language Models (LLMs) operate as essentially stateless systems. They reason inside a temporary "context window" that vanishes when the session ends. If you want an AI that remembers, learns, and personalizes over time, you must design for state. That’s what Context Engineering does: it builds the framework that transforms...

In this post, I explain Introduction to Generative AI with Large Language Models, Key Concepts & Definitions, Underlying Models: Transformers & Beyond, Modeling andTraining Foundations, Sampling & Decoding for Generation Quality, Prompting Strategies for Generative AI (zero-shot, few-shot, chain-of-thought prompting, role prompting, and advanced prompt tactics), Scaling & Emergent Capabilities in Generation, Mitigating Hallucination & Ensuring Output Reliability -RAG and grounding, and Advanced Generation: Multimodality & Specialized Content. If you want my full Gen AI with LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn:  Popular Generative LLMs & Frameworks (GPT-series, Claude, PaLM, Gemini, LLaMA), Efficiency & Deployment Optimization distillation, quantization, parameter-efficient tuning etc.), Ethics, Privacy & Governance, Generative AI Project Workflow (end-to-e...

Symbolic Generative AI Knowledge Bot Symbolic Generative AI Knowledge Bot This is a symbolic AI chatbot designed to provide knowledge about Generative AI concepts, such as LLMs, GANs, Transformers, Datasets, and Applications. This chatbot uses symbolic reasoning to infer answers from a defined knowledge base. Get GitHub code here . Learn how it works on YouTube here . Features Dynamic reasoning based on entities and relationships from the knowledge base. Fallback responses for unmatched queries. Easily extensible knowledge base (in JSON format). Type a query about GenerativeAI (e.g., "Tell me about LLMs"). No capitalization needed! Supported terms: GenerativeAI, Datasets, LLMs, Diffusion Models, GANs, Transformers, Applications, Ethics Send Clear Tip: ask using natural language, e.g., "Tell me about GANs" or "What are the limitations of GenerativeAI?" Small d...

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...