Data & AI

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:

Using RAG is about getting the most from LLMs ability to phrase proper answers and at the same time to make sure it uses the most relevant and up-to-date data according to the user’s question. The objective is to deliver high-quality answers to users.

Nuclia has been building something for the last two years. Our vision is to deliver an engine that allows engineers and builders to search on any domain specific set of data, focusing on unstructured data like video, text, PDFs, links, conversations, layouts and many other sources.

One of the groundbreaking advancements of late in AI is Retrieval-Augmented Generation (RAG), which combines large language models (LLMs) with external knowledge bases to produce more accurate and contextually relevant responses. However, the implementation of RAG systems brings forth new challenges that necessitate robust evaluation models. This article delves into the importance of having an evaluation model when implementing RAG in a business context.