In this guide, we explain what retrieval-augmented generation (RAG) is, specific use cases and how DataStax can help. Learn more here!
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That's a problem we can solve using Retrieval Augmented Generation (RAG). In this blog, I will break down how RAG works, why it’s a game-changer for AI applications, and how businesses are using it to create smarter, more reliable systems. What Is RAG? Retrieval Augmented Generation (...
Using the example of a chatbot, once a user inputs a prompt, RAG summarizes that prompt usingvector embeddings-- which arecommonly managedin vector databases -- keywords or semantic data. The converted data is sent to a search platform to retrieve the requested data, which is then sorted bas...
September 2024 Using Microsoft Fabric for Generative AI: A Guide to Building and Improving RAG Systems This tutorial includes three main notebooks, each covering a crucial aspect of building and optimizing RAG systems in Microsoft Fabric. September 2024 Harness Microsoft Fabric AI Skill to Unlock Cont...
RAG isn’t the only technique used to improve the accuracy of LLM-based generative AI. Another technique is semantic search, which helps the AI system narrow down the meaning of a query by seeking deep understanding of the specific words and phrases in the prompt. ...
RAG seemed like it would be the answer to everything that’s wrong with LLMs. While RAG can help, it isn’t a magical fix. In addition, RAG can introduce its own issues. Finally, as LLMs get better, adding larger context windows and better search integrations, RAG is becoming less ne...
Announcements of new and enhanced features, including a service rename of Azure Cognitive Search to Azure AI Search.
LangChainis an open source framework that facilitates RAG by connecting LLMs with external knowledge sources and that provides the infrastructure for building LLM agents that can execute many of the tasks in RAG and RALM. What are some generative models for natural language processing?
Google released BERT asopen-source software, spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs. Then it applied the technology to its search engine so users could ask questions in simple sentences. ...