Fourth Industrial Revolution

Summary : My new CodeCoach app is a Gemini-powered multimodal application that turns any coding session into instant understanding and career-ready assets. Paste code, upload a screenshot, or record a short voice note — CodeCoach explains your code, suggests improvements and tests, generates interview Questions and Answers, and produces polished resume, LinkedIn, and GitHub text you can use immediately. What is CodeCoach? CodeCoach helps developers, QA engineers, and data practitioners make their daily work visible. Instead of letting valuable fixes, refactors, and experiments disappear into commit history, CodeCoach creates concise technical explanations and ready-to-publish professional artifacts. It combines code understanding with real-world context so you can quickly communicate impact to hiring managers, teammates, and recruiters. View CodeCoach working in action  here . How it works, in a few seconds Use one of three simple inputs: Text : Paste a cod...

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...

Summary : Machine learning algorithms let computers learn from data to make predictions and discover patterns. This post explains the main algorithm types, the typical workflow, and how to choose the right approach for your problem. What Are Machine Learning Algorithms? Machine learning algorithms are sets of procedures a computer follows to learn from data. Instead of being explicitly programmed for every scenario, these algorithms identify patterns, make predictions, and improve as they see more data. The goal is to build models that generalize from past examples to new, unseen situations. 1. Supervised Learning In supervised learning, the training data includes inputs and the correct outputs, known as labels. The algorithm learns a mapping from inputs to outputs so it can predict labels for new examples. Examples : Linear regression — predicts continuous values, such as house prices. Logistic regression and support vector machines — common for classification task...

Summary : An end-to-end AI/ML lifecycle transforms data into production-ready models. This post explains development, fine-tuning, deployment, and continuous optimization with practical steps to keep models accurate, efficient, and reliable. The End-to-End AI/ML Model Lifecycle: From Concept to Continuous Improvement Building useful AI and machine learning systems means moving through a clear lifecycle: development, fine-tuning, deployment, and optimization. Each stage matters, and the lessons learned at the end feed back into the beginning. Below is a practical, readable walkthrough of each stage and the practices that help models succeed in production. Development: Problem, Data, and Baselines Development starts with a clear problem statement and the right data. Define the business objective, determine what success looks like, and gather representative data. Data preparation often takes the most time: clean the data, handle missing values, engineer features, and split the dat...

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...