# Building LLM Applications from Scratch  

🚀 **Build LLM-Powered Applications Like a Pro!**  

Welcome to the Open Sourced version of my course on LLMs.

This course is one of the top-rated technical courses on building **Large Language Model (LLM) applications** from the ground up. So far, I’ve taught this course to **over 1500 professionals**, at MAVEN, Stanford, UCLA and University of Minnesota, helping them gain a deep understanding of **Transformer Architecture**, **Retrieval-Augmented Generation (RAG)**, and **open-source LLM deployment**.  

Unlike most courses that focus on pre-built frameworks like **LangChain**, this course goes beyond by diving into the **building blocks of retrieval systems**, enabling you to **design, build, and deploy your own custom LLM-powered solutions**.  


🌎 **Also featured in Stanford's AI Leadership Series:**  
🔗 [Stanford AI Leadership Series - Building and Scaling AI Solutions](https://continuingstudies.stanford.edu/courses/professional-and-personal-development/the-ai-leadership-series-building-and-scaling-solutions/20243_TECH-103)  

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## 📌 Learning Outcomes  

- Gain a **comprehensive understanding** of LLM architecture  
- **Construct and deploy** real-world applications using LLMs  
- Learn the **fundamentals of search and retrieval** for AI applications  
- Understand **encoder and decoder models** at a deep level  
- Train, fine-tune, and **deploy LLMs for enterprise use cases**  
- Implement **RAG-based architectures** with open-source models  

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## 📢 **Who is This Course For?**  

This course is **not for beginners**. It requires:  
✅ **Python programming skills**  
✅ **Basic machine learning knowledge**  

It is **designed for**:  
🔹 Machine Learning Engineers  
🔹 Data Scientists  
🔹 AI Researchers  
🔹 Software Engineers interested in LLMs  

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## 📌 **What You’ll Learn**  

✔ **Collect and preprocess data** for LLM applications  
✔ **Train and fine-tune pre-trained LLMs** for specific tasks  
✔ **Evaluate model performance** with appropriate metrics  
✔ **Deploy LLM applications** via APIs and Hugging Face  
✔ **Address ethical concerns** in AI development  

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## 📚 **What’s Included?**  

✅ **29 in-depth lessons** covering LLM architectures and RAG techniques  
✅ **6 real-world projects** to apply your learnings  
✅ **Interactive live sessions** and direct instructor access  
✅ **Guided feedback & reflection**  
✅ **Private community of peers**  
✅ **Certificate upon completion**  


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## 📢 Attribution & Credits  

If you use my course material, content, or research in your work, please credit me and the respective contributors.  

🔹 **Proper citation format:**  
> Farooq, H. (2024). *Building LLM Applications from Scratch*  
> Stanford Continuing Studies: *The AI Leadership Series*  

📌 Tagging & mentions are always appreciated! 😊  

## 📅 **Course Syllabus**  

### **Week 1: Introduction to NLP**  
- Understanding natural language processing fundamentals  
- Tokenization, embeddings, and vector representations  

### **Week 2: Transformers & LLM System Design**  
- The evolution of Transformer models  
- Understanding encoder-decoder architectures  

### **Week 3: Semantic Search & Retrieval**  
- Implementing **vector search** for LLM applications  
- Introduction to **RAG-based architectures**  

### **Week 4: Building a Search Engine from Scratch**  
- Developing a **custom RAG solution**  
- Optimizing search and retrieval pipelines  

### **Week 5: The Generation Part of LLMs**  
- Fine-tuning models for text generation tasks  
- Optimizing inference for real-time applications  

### **Week 6: Prompt-Tuning, Fine-Tuning & Local LLMs**  
- Techniques for **efficient inference & quantization**  
- Deploying **custom LLMs** at scale  

🎉 **Post-Course:** **Demo Day** – Present your final project!  

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## ⭐ **What Students Are Saying**  

> _"This course was amazing! I left feeling empowered and ready to build my own LLM-powered applications."_  
> **– Tiffany Teasley, Data Scientist**  

> _"Hamza’s approach to teaching is practical and engaging. The real-world projects made all the difference!"_  
> **– Victor Calderon, Senior ML Engineer**  

> _"One of the best courses for LLM applications! Highly recommended for anyone serious about the field."_  
> **– Abhinav, Security Researcher**  

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## 🔥 **Why Take This Course?**  

Unlike most AI courses that rely on **pre-built frameworks**, this course **teaches you how to build LLM applications from scratch**—without **LangChain or LlamaIndex**.  

By the end, you’ll be able to:  
✅ Build **highly customizable** LLM applications  
✅ Optimize **retrieval and search strategies**  
✅ Deploy **cost-efficient** and **scalable** AI solutions  


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