📄 中文摘要
大型语言模型（LLMs）如 ChatGPT 在写作、编程和回答问题方面表现出色，但有时会出现一些问题，例如“幻觉”（编造事实）、提供过时信息或对特定或私密数据缺乏了解。对于公司最新内部项目的询问，LLM 可能无法提供准确答案。RAG（检索增强生成）技术的出现，旨在提升 LLM 的能力和可靠性。RAG 允许 LLM 在生成回答时，结合外部信息源，从而增强其生成内容的准确性和相关性。
解锁更智能的人工智能：初学者的 RAG（检索增强生成）指南
📄 English Summary
Unlock Smarter AI: A Beginner's Guide to RAG (Retrieval Augmented Generation)
Large Language Models (LLMs) like ChatGPT excel in writing, coding, and answering questions, but they can exhibit quirks such as 'hallucinating' (fabricating facts), providing outdated information, or lacking knowledge about specific or private data. For instance, when asked about a company's latest internal project, an LLM might not have the necessary information. The introduction of Retrieval Augmented Generation (RAG) aims to enhance the capabilities and reliability of LLMs. RAG allows LLMs to access external information sources while generating responses, thereby improving the accuracy and relevance of the content they produce.