What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models (LLMs) by retrieving relevant information from external knowledge bases before generating responses.How RAG Works
1.Query Processing — The user's question is converted into a vector embedding
2.Retrieval — The system searches a knowledge base for the most relevant documents or chunks
3.Augmentation — Retrieved context is combined with the original query
4.Generation — The LLM generates a response grounded in the retrieved information
Why RAG Matters
Traditional LLMs can only rely on their training data, which may be outdated or incomplete. RAG solves this by:•Reducing hallucinations — Responses are grounded in actual source material
•Staying current — The knowledge base can be updated without retraining the model
•Domain specificity — You can point the system at your own data for specialized answers
RAG in Customer Support
Platforms like SiteSupport use RAG to train AI chatbots on your website content. When a visitor asks a question, the system retrieves relevant pages from your site and generates an accurate answer based on your actual content.Key Components
| Component | Purpose ||-----------|---------|
| Embeddings | Convert text into numerical vectors for similarity search |
| Vector Database | Store and query document embeddings efficiently |
| LLM | Generate natural language responses from retrieved context |
| Chunking | Break documents into optimal-sized pieces for retrieval |