Welcome to the Codebasics AI Community 👋 Glad you are here. This is a space for anyone who wants to actually use AI, not just read about it. This is where we go deeper: you learn, you try things, and you share what you build with people working toward the same goal as you. How it works here: 1. Learn from our videos and from what other members are building 2. Try things out and start doing your own work 3. Share what you made, even if it is small or unfinished 4. Help each other and grow together No question is too basic here. If you are stuck, ask. Do this now to get started: 👉 comment to this post with: - Your name and what you do - Where are you from - One task you wish AI could do for you - One thing which is outside of work about you That is it. Say hi, and let us build together.

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.

I've noticed something while learning AI. Knowing AI terms is useful, but asking better questions seems even more important. What's one prompt you've used recently that gave you an unexpectedly great result? I'm always looking to learn better prompting techniques.

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.

I guess I'm the first one to post in the Wins & Progress channel! That counts as a win in itself, right? So, here is my update for Day 1 of the 10-Day AI Kickstart. The task was to ask an AI tool about our line of work and have it explain it like I'm five (which I'm definitely not!). My prompt was: "Explain what a Machine Learning Engineer does like I am five in simple, short terms, and with a small example." I honestly thought it would be a bit difficult to explain the role of ML to a five-year-old, but AI is pretty smart! 😁 Here is the answer the AI gave me: Imagine you have a smart robot 🤖. A Machine Learning Engineer teaches the robot to learn from examples instead of giving it every rule. Simple Example: You show the robot 100 pictures of cats 🐱 and dogs 🐶. "This is a cat." "This is a dog." "This is a cat." After seeing many examples, the robot learns the pattern. Now when you show it a new picture, it can say: "I think this is a cat!" 🐱 The Machine Learning Engineer: Collects the pictures (data). Teaches the computer using that data (trains a model). Checks if it learned correctly. Puts it into an app so people can use it. Pretty cool, right? AI is amazing, but I went a step further and asked it to convert the answer into an infographic since we usually understand things better visually. That's it for me wrapping up the first day!