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Hands-on AI & LLM App Development
Module 1. LLMs Demystified: From Basics to API Integration
What Will You Learn in This Course — And Why Does It Matter? (3:45)
What Exactly Is a Large Language Model (LLM)?
Why Do LLMs Rely on Probability and Not Certainty?
How Do LLMs Actually Learn from Data?
How Does a Large Language Model Work?
What Are the Key Parameters That Shape an LLM’s Output?
What Are Tokens and Why Do They Matter?
Intro to Context Window (4:08)
What Is a Context Window and How Does It Affect Input?
What Is Temperature and How Does It Influence Creativity?
What Is Top-p Sampling and How Is It Used? (2:52)
What Is Top-p Sampling and How Is It Used?
What Is Top-k Sampling?
What’s the Difference Between Top-p and Top-k Sampling?
How to Control Output Length and Quality?
What Does an API Call Actually Cost?
Hugging Face API Installation & Setup Guide
Key Takeaways & Summary
Quiz: Let's Test Your Knowledge
Hands on Project Project
Module 2: Designing Effective Prompts and Building with LangChain
What Makes a Good Prompt Different from a Great One?
What are Prompt Patters (2:16)
What Are Prompt Patterns Like Zero-shot, One-shot, and Few-shot?
How Hallucinations Occur in LLMs and How to Minimize Them?
What Is LangChain and Why Should I Use It?
What Is a Model in LangChain And How To Choose One?
What Is a Prompt in LangChain and How Is It Structured?
What Are Output Parsers and How Do They Help Extract Results?
What Is a Chain in LangChain and How Does It Work?
What Are Indexes in LangChain and When To Use Them?
What Is Memory in LangChain And How Does It Keep Context?
Key Takeaways & Summary
Quiz: Let's Test Your Knowledge
Module 3: Retrieval-Augmented Generation (RAG) with Vector Databases
Why Do LLMs Need External Knowledge to Answer Accurately?
What Are Embeddings and Why Are They Useful?
How Do Embeddings Power Semantic Search?
Why Not Use Traditional Databases for Semantic Search?
What Is a Vector Database and How Does It Work?
What Is Retrieval-Augmented Generation (RAG)?
How Does Embedding-Based Retrieval Work?
How Do Euclidean and Cosine Similarity Compare?
How Are Word Frequencies Turned Into Vectors?
Quiz: Let's Test Your Knowldge
Key Takeaways
Module 4: Build and Deploy an End-to-End LLM App
Frontend (Streamlit or Gradio)
Backend (LangChain, FastAPI optional)
LLMs, retrieval, and streaming
Deployment: Hugging Face Spaces, Streamlit Cloud, or local
Hands-on Project 4: Build and Deploy an AI tool
Module 5: Capstone Project – Deploy Your Own LLM App
Project Overview
What Will You Learn in This Course — And Why Does It Matter?
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