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.

Welcome! Two minutes here and you will know exactly what you have, where to go, and how to climb the leaderboard. What you get (all free) ➡️ A full classroom. Open the Classroom tab. Start with the 10-Day AI Kickstart: one short step a day, no coding. Then browse the topic courses and 4 hands-on project courses (invoice automation, variance-report agent, Notion tracker, Copilot + Gemini productivity). ➡️ A community that helps. Post wins, ask questions, learn from what others build. ➡️ A leaderboard that rewards you for showing up and helping out. Where to post what ➡️ 🏆 Wins & Progress — anything you built, learned, or finished. ➡️ 🆘 Get Help — stuck on a lesson or build? Ask here. ➡️ 👋 Introductions post — say hello in the comments (pinned). ➡️ 📣 Announcements — new courses and videos land here. Turn notifications on. --- The leaderboard: how to level up You earn points when other members like your posts and comments. More points means higher levels and a spot on the weekly, monthly, and all-time leaderboards. ➡️ Post real wins and useful answers (sharing on LinkedIn too? Drop the link or paste your post here so we can celebrate you). Helpful posts get liked. Spammy "give me likes" posts do not. ➡️ Answer questions in 🆘 Get Help. Teaching others is the fastest way up. ➡️ Show up. Activity compounds. ---- Levels: 🌱 AI Curious → 💡 AI Aware → 💬 AI Prompter → 🛠️ AI Practitioner → 🧱 AI Builder → ⚙️ AI Automator → ♟️ AI Strategist → 🚀 AI Leader → 🏆 AI Power User ---- Your first 3 steps 1. Say hello in the 👋 Introductions post. 2. Open the Classroom and start Day 1 of the 10-Day Kickstart. 3. Post your first win in 🏆 Wins & Progress. That is it. Let us build.😎

🚀 DAY 2 LEARNING | 10-DAY AI KICKSTART 🤖✨ 😄 Here's my Day 2 update from the 10-Day AI Kickstart journey! Today, I explored a few fundamental AI concepts that anyone can understand — no technical background required. 📚💡 🔹 Tokens The basic building blocks AI reads and processes. They're not exactly words or sentences, but small chunks of text. 📝 Example: The word "unbelievable" may be split into: ➡️ "un" + "believe" + "able" That's 3 tokens, not 1 word, from the AI's perspective. 🔹 Context Window The amount of information an AI can remember and use at one time. Once that limit is reached, older information starts getting dropped. 📝 Example: Imagine uploading a 200-page contract 📄. If the AI's context window can only hold 50 pages, it may forget details from the beginning while processing the end. 🔹 RAG (Retrieval-Augmented Generation) Instead of relying only on its training, the AI first retrieves relevant information from trusted documents and then generates an answer. 📝 Example: Rather than guessing your company's refund policy, the AI searches your actual policy document 🔍 and answers based on that information. ✅ 💡 Key Takeaway: These may seem like small concepts, but understanding them helps explain why AI behaves the way it does—and how to get better results from it. 📈 Learning a little more every day. 🔥 On to Day 3! #AI #ArtificialIntelligence #MachineLearning #GenerativeAI #AgenticAI #RAG #Tokens #ContextWindow #AILearning #10DayAIKickstart #LearningInPublic #TechLearning #FutureOfWork #Innovation #CareerGrowth 🚀

I guess I'm getting the hang of this Wins & Progress channel now! 😄 Here's my Day update for the 10-Day AI Kickstart. The task was to ask an AI tool to explain the different "layers" of AI like I'm five, since people always mix up AI, Machine Learning, GenAI, and Agentic AI. My prompt was: "Explain the difference between AI, Machine Learning, Deep Learning, Generative AI, and Agentic AI like I am five, in simple short terms with a small example." Honestly I thought this one would be way harder than yesterday's, since even adults at work get these terms confused! But the AI broke it down nicely. 😁 Here's the answer it gave me: Think of AI as one big toy box 🧸. Inside that box are smaller toys that all do different jobs. Machine Learning is a toy that learns from examples, like sorting cat 🐱 and dog 🐶 pictures after seeing lots of them. Deep Learning is a smarter version of that toy, with a brain made of tiny connected dots (like a spiderweb 🕸️) that helps it understand harder things, like photos or voices. Generative AI is the toy that creates new things, like drawing a brand-new picture 🎨 or writing a new story, instead of just sorting what already exists. Agentic AI is the toy that doesn't just answer questions, it actually goes and does the task for you, step by step, like a little robot 🤖 with its own to-do list. Simple way to remember it: - ML/Deep Learning → learns and predicts - GenAI → creates something new - Agentic AI → takes action on its own Pretty wild how layered AI actually is! I asked it to turn the explanation into an infographic too, since visuals just make it click faster for me. That's a wrap for today! 🚀

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.