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 Fine Tuning Large Language Models: Fine Tuning, Transfer Learning, Pretraining vs Fine-Tuning, Dataset Curation, Classification, Generation, Entity Matching, Sequence Instructioning), Annotation, Labeling Strategies & Synthetic Data for Domain Adaptation, Fine-Tuning Workflows, Parameter-Efficient Fine-Tuning, Instruction Tuning & Sequential Instruction Fine-Tuning, RLHF, Reward Modeling, and Safety Tuning, Fine-Tuning for Specialized Use Cases: Domain Adaptation & Entity Matching, Adaptive Machine Translation, Model Architectures & Scaling Considerations for Fine-Tuning, Hyperparameters, Optimizers & Practical Recipes (LR, Schedules, Batch Size), Mixed Precision, Memory Optimization, and Distributed Training. If you want my full Fine Tuning LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn: Tooling & Frameworks, Offline Metrics, Human Evaluation, and Task-Speci...

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...

In this post, I explain Introduction to RAG in LLMs (Large Language Models), RAG Concepts in LLMs, Retrieval Modules and Vector Embeddings, Indexing Strategies and Vector Databases, Document Ingestion and Preprocessing, RAG in LLM Python, RAG Frameworks (such as LangChain and LlamaIndex), Retrieve‑Then‑Generate vs Generate‑Then‑Retrieve, Prompt Engineering for RAG and Evaluation Metrics for RAG. 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 Retrieval-Augmented Generation (RAG) Framework in LLMs document that additionally includes the following important topics, you can message me on LinkedIn : Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge. Question : What does RAG stand for in...