Today's challenge was to ask AI a question related to my work or career. The question I asked: "Can you explain what a Data Engineer does as if I were six years old?" The response in simple words: AI compared a Data Engineer to someone who builds roads and pipes for data. It explained that data comes from many different places, and a Data Engineer makes sure it is collected, cleaned, and delivered safely so other people can use it to make smart decisions. What I found interesting: The explanation was easy to picture because it used a real-life example instead of technical terms. It made a role that sounded complicated feel much more approachable. My biggest takeaway: AI isn't only useful for getting information—it's also a great tool for learning new concepts in a way that's easy to understand. ✅ Day 1 completed!

My mom always says, "If you learn something early in the morning, you tend to remember it for longer." So today, I decided to learn some AI jargon. Task : pick 3 terms from the video. In your own words, write a one-line meaning for each. If you cannot, ask your AI chat to explain it, then rewrite it your way. What I did is i first explain my answer to the ai tool and asked him to improve it based on the context and then asked him to make it one liner My Prompt: Prompt : It is the command we give to llm. RAG: It is a technique which retrieves answer by looking at knowledge base without memorizing or learning for it hence it is fast. AI Agents/Agentic AI : AI agents not only tells how to do it but can also do the task on your behalf. Response from AI : 1. Prompt A prompt is the instruction, question, or context we give an LLM to tell it what we want. 2. RAG (Retrieval-Augmented Generation) RAG is a technique where an AI retrieves relevant information from an external knowledge base before generating a response. 3. AI Agents (Agentic AI) Definition (One-liner): An AI agent is an AI system that can plan, make decisions, use tools, and complete tasks on your behalf. Woohooo AI is way smarter than me 😁 I also made some handwritten notes because, as mom says, "If you write things down, you understand and remember them better." Once again, my mom's advice came in handy.

Let's Simplify AI jargons. 🙂 Three new AI jargons which I have understood clearly today. 1- Context Window : The amount of data which a LLM considers. 2- Model Context Protocol (MCP) : The way a LLM is integrated with a tool or application. For example : connecting Email to Claude. 3- AI Hallucination : When AI confidently gives answer of a problem but it's wrong. Two bonus terms for today as learning AI from scratch is interesting . 1- Retrieval Augmented Generation (RAG) : The LLMs which gives answer based on some reference data not from their ow leanring. 2- AI Agents/Agentic AI : AI with hands not just mouth. LLMs which are more than just question , answer chatbots. They can perform task on our behalf. Like : Amazon Rufus. The new terms which I learned today are Context window and MCP 🙂

Task: Kill the jargon — explain 3 AI terms in simple words. My 3 Terms: - Context Window : The amount of text an AI can consider at one time while generating a response. - Model Context Protocol (MCP): A standard way for connecting LLMs with different tools and applications like emails, calendars, etc. - Retrieval Augmented Generation (RAG): A method where AI first retrieves relevant information from external sources and then uses it to generate more accurate responses. Result: These concepts become much easier to understand when broken down into simple, practical meanings.

Learned very basic what and Whys of AI: What are models and what they contain? What is done during training phase of a model? What is called Inference ? What is generative AI? What is Agentic AI? What are LLMs ? Different types of LLMs? What is Machine Learning ? Types of it? Got the feeling of reopening my neural networks which were built during studying a college course of DATA Mining. :) One question asked to chatgpt: Question: Tell me about Data Analyst and Data Engineers work like I'm five. Ans: Imagine you're 5 years old. 😊 You are building with LEGO. On the floor, you have 10,000 LEGO pieces mixed together. To build a castle, what do you do? First, you look at the pieces. 👀 Then you sort them by color and size. 🟦🟥🟨 Then you follow instructions. 📖 Finally, you build the castle. 🏰 Being a Data Analyst or Data Engineer is almost the same. Data is just like LEGO pieces Imagine a company sells toys. Every day, they collect information like: Someone bought a teddy bear. Someone returned a toy car. Someone paid ₹500. Someone visited the website. After one year, they have millions of these little pieces of information. It's a huge messy LEGO box. What does a Data Engineer do? Think of a Data Engineer as the person who cleans the room before anyone plays. They say: "Let's put all red LEGO together." "Let's remove broken pieces." "Let's keep everything in the right boxes." In the data world, this means: Collecting data Cleaning data Moving data Storing data safely Making sure it arrives quickly Without them... ❌ The data is messy. What does a Data Analyst do? Now the room is clean. The Data Analyst comes in and asks: "Hmm... what can these LEGO pieces tell us?" For example: Which toy sold the most? Which city bought the most toys? Why did sales drop this month? Which customers spend the most? Then they make beautiful charts so everyone understands. So why is learning important? Imagine you want to become the world's best LEGO builder.