The big ideas behind retrieval augmented generation

This post explains the core concepts behind Retrieval Augmented Generation (RAG), including the challenges of Large Language Models (LLMs) like hallucination and outdated data. It details how semantic search, powered by vector embeddings, addresses these limitations by enabling LLMs to access and utilize private data. The post contrasts RAG with training LLMs from scratch and fine-tuning, highlighting RAG's practicality and scalability. It also touches upon the cost implications of token usage in LLM interactions and the importance of efficient data retrieval for prompt engineering.