Implementing RAG with LlamaIndex

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating an external knowledge retrieval process. Unlike standalone LLMs, which generate responses based purely on pretrained knowledge, RAG dynamically fetches relevant information from external sources before generating a response. This process involves three key components: indexing, retrieval, and augmented generation.