The big ideas behind retrieval augmented generation

What this post added

This post elaborates on the foundational concepts of retrieval augmented generation (RAG) and semantic search. It explains how LLMs can be enhanced by grounding their responses in specific data sources, addressing challenges like data limitations and hallucinations. The post details the transition from keyword search to semantic search by representing text as numerical vectors (embeddings) in a multi-dimensional space. It illustrates this concept with examples and discusses the practical implications for building RAG applications, emphasizing the need for efficient data retrieval to optimize LLM prompts and manage costs. The post also touches upon the limitations of training LLMs from scratch or fine-tuning existing ones, positioning RAG as a more accessible and scalable approach.