This paper reviews retrieval-augmented generation (RAG) techniques for natural language processing (NLP). RAG leverages external knowledge databases to enhance large language models (LLMs), addressing issues like hallucination, knowledge update, and domain-specific expertise. The paper systematically introduces RAG components, including retrievers, retrieval fusions, and generators. It discusses RAG training strategies, both with and without datastore updates, and explores applications in NLP tasks and industrial scenarios. The paper also identifies future directions and challenges for RAG. Key components of RAG include the retriever, which involves encoding, indexing, and querying external knowledge, and retrieval fusions, which integrate retrieved information into generation. The paper provides tutorial codes for implementing RAG techniques and discusses various fusion methods, including query-based, logits-based, and latent fusions. It also covers generator types, such as default and retrieval-augmented generators, and discusses training strategies for RAG. The paper highlights the importance of RAG in improving the accuracy, knowledge, and reliability of LLMs in various NLP applications.This paper reviews retrieval-augmented generation (RAG) techniques for natural language processing (NLP). RAG leverages external knowledge databases to enhance large language models (LLMs), addressing issues like hallucination, knowledge update, and domain-specific expertise. The paper systematically introduces RAG components, including retrievers, retrieval fusions, and generators. It discusses RAG training strategies, both with and without datastore updates, and explores applications in NLP tasks and industrial scenarios. The paper also identifies future directions and challenges for RAG. Key components of RAG include the retriever, which involves encoding, indexing, and querying external knowledge, and retrieval fusions, which integrate retrieved information into generation. The paper provides tutorial codes for implementing RAG techniques and discusses various fusion methods, including query-based, logits-based, and latent fusions. It also covers generator types, such as default and retrieval-augmented generators, and discusses training strategies for RAG. The paper highlights the importance of RAG in improving the accuracy, knowledge, and reliability of LLMs in various NLP applications.