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.
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</> Related Terms
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.
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.