RAG Foundations Byte

Retrieval-Augmented Generation (RAG) feeds relevant external documents into an LLM's prompt context before generating a response.

This prevents hallucinations and bypasses static model training limits.

📊 RAG Architecture

User Query ──┬──► [ Vector Store Search ]
             │            │
             │      (Retrieves Context Documents)
             ▼            ▼
         [ Formatted Context Prompt ] ──► [ LLM Generation ] ──► Response

1. Ingestion: Split documents into chunks, convert them to vector embeddings, and store them in a vector database.
2. Retrieval: Use similarity search (like Cosine distance) to find vector chunks closest to the user's query.
3. Generation: Combine the user query and retrieved document context into a prompt, allowing the LLM to write an accurate answer grounded in your documents.