RAG for Generative AI Applications Specialization Course Syllabus

Overview: This end-to-end specialization equips learners with practical skills to build production-ready Retrieval-Augmented Generation (RAG) applications using leading frameworks and vector databases. Spanning approximately 24 hours across four core courses, the program combines foundational concepts with hands-on labs in LangChain, LlamaIndex, FAISS, ChromaDB, Flask, and Gradio. You'll progress from GenAI fundamentals to advanced retrieval techniques, culminating in a complete RAG-powered application. Designed for intermediate practitioners, this course emphasizes real-world implementation and integration patterns used in enterprise AI systems.

Module 1: Develop Generative AI Applications: Get Started

Estimated time: 8 hours

• Generative AI fundamentals
• LangChain prompt templates
• Flask integration for web interfaces
• Model selection and structured JSON output generation

Module 2: Build RAG Applications: Get Started

Estimated time: 6 hours

• Retrieval-Augmented Generation (RAG) architecture
• Gradio interface development
• LangChain vs. LlamaIndex comparison and use cases
• Document-based question answering with RAG workflows

Module 3: Vector Databases for RAG: An Introduction

Estimated time: 9 hours

• Vector databases vs. relational databases
• ChromaDB setup and operations
• Similarity search implementation
• Building recommendation systems with vector embeddings

Module 4: Advanced RAG with Vector Databases and Retrievers

Estimated time: 1 hour

• Advanced retrieval patterns in RAG
• FAISS retriever optimization
• End-to-end RAG application design with Gradio UI

Module 5: Final Project

Estimated time: 4 hours

• Design and implement a full RAG-powered web application
• Integrate vector database (ChromaDB or FAISS) with retrieval pipeline
• Deploy application interface using Gradio or Flask

Prerequisites

• Intermediate Python programming skills
• Familiarity with machine learning and AI concepts
• Basic understanding of LLMs and API integration

What You'll Be Able to Do After

• Build and deploy GenAI applications using LangChain and LlamaIndex
• Implement RAG pipelines for improved response accuracy
• Use vector databases like ChromaDB and FAISS for semantic search
• Develop interactive UIs with Gradio and Flask for RAG apps
• Apply retrieval optimization techniques in real-world scenarios