Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies

The article explores the rapid development of artificial intelligence (AI) and the need for technologies that overcome the limitations of large language models (LLMs), such as knowledge boundaries, hallucinations, and data security concerns.

It introduces Retrieval-Augmented Generation (RAG) as a framework that combines retrieval from vector databases with generation by LLMs to provide more accurate, up-to-date, and explainable answers. The workflow involves data preparation, retrieval, and answer generation, with discussions on vector database options (Faiss, Annoy, HNSW, Elasticsearch, Milvus, Pinecone, Weaviate, Vectara) and optimization trends in storage, recall, system architecture, hardware acceleration, model updates, and embedding techniques.

Additionally, the piece covers multi-agent systems (AutoGen, MetaGPT) for collaborative task decomposition, prompt engineering strategies to improve LLM outputs, and brief notes on model fine‑tuning methods like PEFT and LoRA. References to LangChain and other AI application frameworks are provided.