RAG 技術可用於改善生成式 AI 系統對提示的回應品質,超越 LLM 單獨提供的程度。優點如下: RAG 能夠存取的資訊,可能比用來訓練 LLM 的資料還要新。 RAG 知識儲存庫中的資料可以持續更新,而不會產生高昂成本。 RAG 知識儲存庫中的資料,可能比一般 LLM 中的資料更符合情境。 可以找到 RAG 向量資料庫中的資訊來源。
chatbots and other conversational systems might use RAG to make sure their answers to customers’ questions are based on current information about inventory, the buyer’s preferences, and previous purchases, and to exclude information that is out-of-date or irrelevant to the LLM’s intended operat...
Retrieval-augmented generation (RAG) is an AI framework that retrieves data from external sources of knowledge to improve the quality of responses. This natural language processing (NLP) technique is commonly used to make large language models (LLMs) more accurate and up to date. LLMs are AI ...
Interaction with Retriever:The generator doesn’t work in isolation; it uses the context provided by the retriever to inform its response, ensuring that the output is not just plausible but also rich in detail and accuracy. The Workflow of a Retrieval-Augmented Generation (RAG) System ...
So, What Is Retrieval-Augmented Generation (RAG)? Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. In other words, it fills a gap in how LLMs work. Under the hood, LLMs are neural...
What is Boolean? In computing, the term Boolean means a result that can only have one of two possible values: true or false. Boolean logic takes two statements or expressions and applies a logical operator to generate a Boolean value that can be either true or false. To return the result...
index.query("What is RAG?") Thus RAG addresses two problems with large language models: out-of-date training sets and reference documents that exceed the LLMs’ context windows. By combining retrieval of current information, vectorization, augmentation of the information using vector similarity searc...
Andrew McKenna, VP of Product Management at DataMotion explained that the goal is to allow users to rapidly find answers in a self-service manner while keeping the human connection only a click away. Since RAG produces relevant answers from data not in the LLMs training dataset, it eliminates...
RAG is a cost-efficient method for supplementing an LLM with domain-specific knowledge that wasn’t part of its pretraining. RAG makes it possible for a chatbot to accurately answer questions related to a specific field or business without retraining the model. Knowledge documents are stored in ...
That is where LlamaIndex provides the toolbox to unlock this data with tools for data ingestion and data indexing. Once ingested and indexed,retrieval augmented generation(RAG) applications can use the LlamaIndex query interface for accessing that data and powering LLMs. ...