Are you...
🤯 Overwhelmed by all the tools and frameworks in the LLM space and don’t know where to start?
🔍 Struggling to turn your AI ideas into working prototypes?
🧩 Confused by LangChain, vector databases, and prompt engineering?
🚫 Tired of tutorials that only show hello world examples with no real use cases?
👀 Watching others build cool AI projects while you’re stuck in analysis paralysis?
🤖 Curious how people are building chatbots, content tools, or agents—but can’t connect the dots yourself?
⚙️ Drowning in APIs like OpenAI, Hugging Face, or Ollama, without knowing how to use them in a full-stack app?
💼 Want 4 solid LLM projects to boost your resume and portfolio?
🚀 Ready to build and deploy real-world apps with LLMs but need a step-by-step guide?
If any of these sound like you, it’s time to roll up your sleeves and build your first (or best) AI-powered app—start to finish. 🛠️✨
Projects You Will Build
This course is focused on learning by doing. Instead of just watching tutorials, you'll build four real-world AI applications using industry tools and frameworks.
Here’s what you’ll create:
- AI Tutor App Powered by Hugging Face and Gradio: You will create an interactive tutor that answers questions on any topic.
- Automated Market Research Generator Using OpenAI API and Streamlit: You will convert raw inputs into full market research reports in just a few clicks.
- Personal Finance Tracker QA System Built with OpenAI API, FAISS, and Streamlit: You will develop a tool that can understand your financial data and answer questions on demand.
- Medical Wellness Assistant Developed using OpenAI, Pinecone, Flask, HTML, and CSS: You will design an LLM assistant that provides health-related support and information retrieval.
💡 If you're ready to stop reading about AI and start building it, this course is your next step.
Curriculum
- 1.1 What Will You Learn in This Course And Why Does It Matter? (3:45)
- 1.2 What Exactly Is a Large Language Model (LLM)?
- 1.3 Why Do LLMs Rely on Probability and Not Certainty?
- 1.4 Behind the Magic: How LLMs Learn Overview (4:06)
- 1.5 How Do LLMs Actually Learn from Data?
- 1.6 How Does a Large Language Model Work?
- 1.7 What Are the Key Parameters That Shape an LLM’s Output?
- 1.8 What Are Tokens and Why Do They Matter?
- 1.9 From Tokens to Context: How LLMs Process Input (4:08)
- 1.10 What Is a Context Window and How Does It Affect Input?
- 1.11 What Is Temperature and How Does It Influence Creativity?
- 1.12 Why LLMs Don’t Always Pick the Top Word? (2:52)
- 1.13 What Is Top-p Sampling and How Is It Used?
- 1.14 What Is Top-k Sampling?
- 1.15 What’s the Difference Between Top-p and Top-k Sampling?
- 1.16 How to Control Output Length and Quality?
- 1.17 What Does an API Call Actually Cost?
- 1.18 API Key Setup Guide: From Hugging Face to OpenAI in Colab
- 1.19 Key Takeaways & Summary
- 1.20 Quiz: Let's Test Your Knowledge
- 1.21 Hands-on Examples & Project
- 2.1 What Makes a Good Prompt Different from a Great One?
- 2.2 Prompt Patterns Explained: Zero-shot to Few-shot (2:16)
- 2.3 What Are Prompt Patterns Like Zero-shot, One-shot, and Few-shot?
- 2.4 How Hallucinations Occur in LLMs and How to Minimize Them?
- 2.5 What Is LangChain and Why Should I Use It?
- 2.6 What Is a Model in LangChain And How To Choose One?
- 2.7 From Template to Prompt: How LangChain Structures Inputs (2:51)
- 2.8 What Is a Prompt in LangChain and How Is It Structured?
- 2.9 What Are Output Parsers and How Do They Help Extract Results?
- 2.10 What Is a Chain in LangChain and How Does It Work?
- 2.11 What Are Indexes in LangChain and When To Use Them?
- 2.12 LangChain’s Brain: What Memory Modules Really Do (2:44)
- 2.13 What Is Memory in LangChain And How Does It Keep Context?
- 2.14 Key Takeaways & Summary
- 2.15 Quiz: Let's Test Your Knowledge
- 2.16 Hands-on examples & Project
- 3.1 What LLMs Don’t Know and Why External Data Helps (2:09)
- 3.2 Why Do LLMs Need External Knowledge to Answer Accurately?
- 3.3 What Are Embeddings and Why Are They Useful?
- 3.4 How Do Embeddings Power Semantic Search?
- 3.5 Why SQL Doesn't Work for Semantic Search? (3:13)
- 3.6 Why Not Use Traditional Databases for Semantic Search?
- 3.7 What Is a Vector Database and How Does It Work?
- 3.8 What Is Retrieval-Augmented Generation (RAG)?
- 3.9 How Does Embedding-Based Retrieval Work?
- 3.10 How Do Euclidean and Cosine Similarity Compare?
- 3.11 How Text Becomes Math: Frequency-Based Embeddings? (3:28)
- 3.12 How Are Word Frequencies Turned Into Vectors?
- 3.13 Key Takeaways & Summary
- 3.14 Quiz: Let's Test Your Knowldge
- 3.15 Hands-on examples & Project
WHO IS THIS COURSE FOR?💻
This course is for anyone who wants to build real-world AI apps—with code to show for it. The only requirement? You know some Python.
🧠 Tech Professionals & Tinkerers: Software engineers, data scientists, ML enthusiasts—learn how to actually build and ship LLM-powered tools.
👶 New to AI or Non-Tech Background? No problem. If you can code in Python, we’ll guide you through APIs, LangChain, and LLM workflows step by step.
📈 Upskillers & Career Switchers: Want to pivot into AI or stand out in a crowded job market? Build 5 polished projects you can add to your resume or GitHub.
🚀 Product Builders & Founders: Got an idea? This course shows you how to bring it to life using LLMs, LangChain, and real-world tools.
🛠️ Hackathon Junkies & Side Project Nerds: Need project inspiration or structure? This is your go-to for building something that actually works.
If you’re ready to stop just reading about AI and start building it—this is your course.