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? (3:13)
- 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 Google 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.
👉 Wondering what the prerequisites are?
You need basic Python skills, but no prior AI knowledge is required.
If you’re ready to stop just reading about AI and start building it, this is your course.
What Students Are Saying ⭐️
AH, AI Engineer
"The AI Engineering Course was exactly what I needed to bridge the gap between theory and real-world AI workflows. The curriculum is thoughtfully structured covering everything from foundational concepts to hands-on implementation using modern tools and frameworks.
By the end of the course, I felt confident designing, building, and maintaining AI-powered solutions and I now have a toolkit I can apply directly to my work. I highly recommend this course to anyone serious about stepping into AI engineering with a strong, practical foundation."
SZ, Senior Software Engineer
"Overall, this is a well structured course that offers clear insights into what LLMs are, how they work, and how to build applications using them.
The introduction to frameworks like LangChain adds practical depth, and the section on RAGs provides a solid foundation with a hands-on implementation.
The course flows logically from concepts to application, making it accessible even for beginners."
Maya K., Product Designer
"I come from a product and design background, so I was nervous about jumping into AI development. But this course made it approachable without dumbing anything down.
The explanations were clear, the code was easy to follow, and by the second project, I was already thinking of ways to build tools for my own startup. It’s the perfect course if you’re technical enough to write Python and curious enough to dive into the AI space."