RAG AI agent (Retrieval-Augmented Generation)

Topic Covered:

🔹Powerful use cases
🔹How a RAG AI Agent Works
🔹Key Components
🔹Advantages of RAG Over Pure LLMs
🔹 Tech Stack for Building a RAG AI Agent
🔹Build a RAG AI Agent using LangChain, OpenAI API, and ChromaDB as the vector database
🔹RAG AI agent as an API using FastAPI and then discuss integrating it into a frontend app.

A Retrieval-Augmented Generation (RAG) AI agent is an AI system that enhances the response generation capabilities of large language models (LLMs) by retrieving relevant information from an external knowledge base. This approach improves accuracy, reduces hallucinations, and enables real-time updates.

Powerful use cases:

1️⃣ AI-Powered Product Search & Discovery

✅ Use Case: Customers often struggle to find the right products, especially with complex queries.
🔹 How RAG Helps:

• Enhances product search by retrieving real-time product catalogs and generating relevant summaries.
• Supports natural language queries like:
• 🛒 “Show me running shoes with high arch support under $100.”
• 🏋️ “Find a smartwatch with a battery life of more than 10 days and water resistance.”