Enhancing Retrieval with Hypothetical Document Embeddings (HyDE) in RAG Systems
Find the paper here RAG The Retrieval-Augmented Generation (RAG) technique offers a promising approach when leveraging large language models like LLMs to build knowledge bases. Envision creating a chatbot capable of querying a collection of textbooks. A standard pre-trained LLM doesn’t inherently possess this capability. This is where RAG comes into play. RAG works by dissecting your corpus into more manageable segments or documents. Ideally, these segments should fit within the context window of the language model in use....