Generative AI, particularly Language Model Machines (LLMs), have become integral to various applications in artificial intelligence. Although these models have impressive capabilities to understand and generate human-like text, they suffer from hallucination and can confidently produce false information. The most powerful LLMs are closed-source and accessible only through APIs, making them black boxes. However, a new approach called Retrieval-Augmented Generation (RAG) reduces reliance on LLMs by utilizing a retriever to retrieve relevant representations from a knowledge base and then using the LLM to generate responses. This modular system provides more control, interpretability, and cost-effectiveness compared to end-to-end LLM models. LanceDB, an open-source embedded vector database, simplifies the retrieval and management of embeddings, offering scalability, efficiency, and integration with existing APIs.
https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984