Retrieval-Augmented Generation (RAG) explained

Retrieval-Augmented Generation (RAG) is an AI technique that enhances Large Language Models (LLMs) by connecting them to external, authoritative data sources. Instead of relying solely on pre-trained knowledge, a RAG system first searches a specific knowledge base for relevant information and then provides that context to the model to formulate a response.

This process significantly reduces AI hallucinations and allows models to access up-to-date or domain-specific information without the need for expensive retraining or fine-tuning. A typical workflow involves preparing documents, breaking them into smaller text chunks, converting those chunks into vector embeddings for storage in a vector database, and finally retrieving the most semantically relevant sections to answer a user query.

Practical implementation often utilizes tools such as n8n for orchestration, OpenAI for generating answers and embeddings, and vector databases like Pinecone for storage.