RAG System Architecture Overview
Understand the components and workflow of a typical RAG system, highlighting the role of vector databases.
What is RAG?
Welcome! In this lesson, we'll explore Retrieval Augmented Generation (RAG) systems. RAG is a powerful technique that combines large language models (LLMs) with external knowledge sources.
It allows LLMs to generate more accurate, up-to-date, and context-rich responses by retrieving relevant information before generating an answer. Think of it as giving an LLM a personal research assistant!
LLM's Knowledge Gap
Large Language Models (LLMs) are amazing, but they have limitations:
- Knowledge Cutoff: Their training data is static, so they don't know about recent events or information.
- Hallucinations: They can sometimes generate plausible-sounding but factually incorrect information.
- Domain Specificity: They lack deep knowledge about private, proprietary, or highly specialized data.
RAG helps address these challenges by providing real-time, relevant facts.
All lessons in this course
- RAG System Architecture Overview
- Integrating with LLM Frameworks
- Contextual Information Retrieval
- Chunking Strategies for RAG