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3 ways how most AI systems are built. I’ve explained each one step-by-step. 1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of example

People think these 3 AI terms are same, they're not. I’ve explained differences for each. 1. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 Using AI to create content across text, images, audio, and video. • 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴-𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 + 𝗟𝗮𝘁𝗲𝗻𝘁 𝗦𝗽𝗮𝗰𝗲:

LangGraph vs Autogen vs CrewAI, which to choose? I've given a breakdown of which is best for you. 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀) LangGraph is ideal for 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼

2 ways AI systems today generate smarter answers. I’ve explained both in simple steps below. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) (𝘴𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) RAG lets AI fetch and use real-time external information to ge

Your AI sucks because it’s stuck at Level 1. You can easily take it to Level 3. I've explained below. 𝗦𝘁𝗲𝗽 1 – 𝗕𝗮𝘀𝗶𝗰 𝗟𝗟𝗠 (𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴) • This is the simplest level of AI systems. • You give input text or a docu

Simple explanation of Traditional RAG vs Agentic RAG vs MCP. 1. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) • 𝗦𝘁𝗲𝗽 1: 𝗨𝘀𝗲𝗿 𝗮𝘀𝗸𝘀 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. Example: “𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘤𝘢𝘱𝘪�

Steps to building Agentic AI systems from scratch. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗚𝗣𝗨/𝗖𝗣𝗨 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿 (Compute Layer) • This is the engine that powers all AI computations. • You rent computing power to run y

If you’re building AI agents today, here’s the reality: Calling an LLM isn’t enough anymore. Modern agents need a full system, a framework of interconnected components that help them think, reason, act, adapt, and collaborate autonomously. Here are

Most people even today don't know these skills. I've explained in very simple below. 1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Learning how to give clear, structured, and goal-driven instructions to AI models to get accurate and useful outputs. Eg: Ch

7 database types used in modern AI systems. I’ve explained each one in simple steps. 1. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 • 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Store embeddings so AI can search by meaning. • 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Text/images → vectors → neare