Eudald Camprubi

Developers, product owners and companies searching for a RAG-as-a-Service solution, listen up! The Nuclia Prompt Lab is here to revolutionize the way you test and integrate LLMs with your unstructured data. The Nuclia Prompt Lab is a unique tool that allows you to test how different LLMs behave and answer questions with your own prompts and data. Say goodbye to hours of experiments, and hello to increased product value.

Unlock the power of Nuclia-managed RAG-as-a-Service (Retrieval-Augmented Generation). Nuclia is the all-in-one platform that seamlessly indexes unstructured data from internal and external sources while facilitating 100% data governance. Nuclia RAG-as-a-Service delivers actionable insights and trusted answers in any language and from any data source.

RAG-as-a-Service (Retrieval Augmented Generation) is now available on the AWS Marketplace. At Nuclia, we’re dedicated to revolutionizing the way businesses leverage artificial intelligence (AI) to extract insights from their data. With RAG-as-a-Service, we’re taking a giant leap forward in empowering companies to unlock the full potential of their unstructured data.

Nuclia-managed RAG, combined with ChatGPT at Azure’s infrastructure, takes your search and find experience to new heights by generating AI-powered answers from unstructured data. Leverage ChatGPT’s sophisticated language understanding and generation capabilities to reveal valuable insights without manual effort.

One of the most powerful aspects of Nuclia is its ability to find the perfect combination of embeddings, LLMs, prompts, and retrieval strategies tailored to any specific use case. Whether you are dealing with highly specialized industry data or general-purpose information, Nuclia’s flexible framework allows you to configure and optimize every aspect of your RAG process. This means that no matter your specific requirements, you can rely on Nuclia to deliver the most effective and efficient results.

Nuclia’s RAG evaluation tools, REMi and nuclia-eval, can be used to identify and resolve issues in a failing RAG pipeline. The tools are based on the RAG Triad framework, which evaluates the query, contexts, and answer in relation to each other using these metrics: