RAG is a pattern that enhances Large Language Models by retrieving relevant information from a knowledge base before generating a response. This allows LLMs to answer questions using specific, up-to-date information without retraining. The process involves: 1) Embedding documents into vectors, 2) Storing in a vector database, 3) Searching for relevant context based on user query, 4) Passing context to LLM for grounded response generation.
🧠 AI & LLMs + 🤖 AI Agents intermediate
RAG (Retrieval-Augmented Generation)
AI technique combining vector search with LLMs to provide contextual answers from custom knowledge bases.
62
views
</> Related Terms
LLM (Large Language Model)
AI models trained on massive text datasets to understand and generate human-like text.
pgvector
PostgreSQL extension that adds vector similarity search capabilities for AI and machine learning applications.
Vector Database
Specialized database for storing and searching high-dimensional vector embeddings.
Embedding
Numerical vector representation of text, images, or other data for machine learning.