As I discussed in my last MarTech article, there are also a number of compelling GenAI use cases related to working with graphics, images, and other digital assets.The requirement for RAG is very ...

Les avantages de l'utilisation du RAG sont évidents. Bien que les LLM soient puissants, ils manquent d'informations spécifiques aux produits, aux services et aux projets de votre entreprise.

When a user submits a query, a RAG system first retrieves the most relevant information from a curated knowledge base. It then feeds this information, along with the original query, into the LLM.

Bloomberg’s paper, ‘RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models,’ evaluated 11 popular LLMs including Claude-3.5-Sonnet, Llama-3-8B ...

Implementing reflection in RAG involves structured steps like relevance filtering, helpfulness assessment, and automated evaluation, creating a feedback loop for continuous improvement.

Vectara’s new Open RAG Eval framework, developed in conjunction with researchers from the University of Waterloo, allows enterprise users to evaluate response quality for each component and ...

Its specialized RAG agents easily surpassed the performance of well-known frontier models like OpenAI’s GPT-4o and Anthropic PBC’s Claude 3.5 Sonnet in areas such as document understanding ...

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Infinidat has launched a retrieval augmented generation (RAG) workflow architecture, deliverable as a consultancy service to its storage customers, which allows them to build in up-to-date, ...

As teams push the first wave of RAG applications into production and get comfortable with LLMs, they should look for educational resources about new techniques like agentic RAG or Generative ...

RAG is a process that improves the accuracy, currency and context of LLMs like GPT4. They work by combining a pre-trained LLM with a retrieval component that is connected to readily accessible ...