Carles Onielfa

Often, questions over a knowledge base require more information than that explicitly present in the documents. For instance, a complex question like“Which lawyers have worked on a case with a senior partner and also specialize in contract law?“ Answering this question would require extracting information from multiple sources and reasoning over it. To tackle such […]

Even though REMi v0 was the first of its kind, since then other models of similar size have been released that have RAG evaluation capabilities. One of those models is IBM Granite-Guardian 3.1, which can evaluate multiple risks, three of these risks are the answer relevance, context relevance and groundedness in a RAG context. In

At Nuclia, our commitment to innovation drives us to continually refine the tools and technologies we provide to our clients. Today, we’re excited to introduce Llama-REMi v1, the successor to REMi v0, our groundbreaking open-source Retrieval Augmented Generation (RAG) evaluation model. While REMi v0 set a high standard for RAG evaluation, Llama-REMi v1 takes this

Retrieval Augmented Generation (RAG) methods have become the go-to technology for document search, utilizing the latest advancements in Large Language Models (LLMs) and Deep Learning. However, evaluating these complex systems is challenging due to the intricate nature of LLM outputs. At Nuclia, we’ve tackled this with REMi, our efficient open-source LLM fine-tuned specifically for RAG