Module 1: Understanding RAG Fundamentals
Module 1: AI Fundamentals Made Simple - RAG for Modern Applications
Master AI application development fundamentals with RAG architecture. Perfect for beginners - learn embeddings, vector databases, and semantic search without any prior AI experience.
Learning Objectives
By the end of this module, you will be able to:- Explain what RAG is and why it’s important for AI applications
- Understand the relationship between embeddings, vector databases, and semantic search
- Describe the chunking process and its role in RAG systems
- Identify real-world use cases for RAG applications
Module Structure
1.1 Conceptual Foundation
Duration: 25 minutes
Learn what RAG is, why it matters, and understand the RAG architecture flow.
Learn what RAG is, why it matters, and understand the RAG architecture flow.
1.2 Vector Space Concepts & Similarity Metrics
Duration: 25 minutes
Explore how text is converted to numerical vectors and how similarity metrics enable semantic search.
Explore how text is converted to numerical vectors and how similarity metrics enable semantic search.
1.3 Chunking Strategies & Performance Optimization
Duration: 20 minutes
Learn how to effectively chunk documents for embedding and optimize performance for large-scale RAG systems.
Learn how to effectively chunk documents for embedding and optimize performance for large-scale RAG systems.
Prerequisites
Before starting this module, ensure you have:- Basic understanding of JavaScript/TypeScript
- Familiarity with React concepts
- No prior AI or RAG experience required
Pro Tip: Take notes as you progress through each section. The concepts
build upon each other, and you’ll want to reference these fundamentals
throughout the rest of the course.