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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

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�

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

4 ways how AI systems communicate and coordinate. I've explained each one in detail below. 1. 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) • User submits a request: “Summarize today’s Slack messages.” • MCP Client receives input: Interface b

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

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

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

The complete AI, ML & GenAI roadmap. I've given a stepwise breakdown to master them. 𝗦𝘁𝗲𝗽 1 – 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (1.5–2 𝗺𝗼𝗻𝘁𝗵𝘀) • Build core skills: Python, math, data handling, Git. • Learn 𝗡𝘂𝗺𝗣𝘆, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶�

5 Layers of AI Product Development. I've explained each in short, simple steps below. 1. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Handles everything the user sees and interacts with → Ensures login security and smooth user experience → Stores session co

Hands down the simplest explanation of AI agents using LLMs, memory, and tools. A user sends an input → the system (agent) builds a prompt and may call tools and memory-search (RAG) → agent decides and builds an answer → the answer is returned to th