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The answer provided by a RAG solution like Nuclia will always depend on how precise the question is. The more information you provide, the more accurate the answer will be. Unfortunately, the user might assume that the system has more context than it actually does. For example, imagine your company is running an online avatar creation studio and you provide the following details about the different subscription plans:

Deep dive into the computational economics of different AI memory approaches from an implementation standpoint.

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.

Take this personality quiz to discover your file transfer mascot. Are you a Security Sphinx or a Chaos Gremlin? Find out which method matches your style and why MOVEit software might be your perfect match.

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.

This blog highlights how thoughtful UX and UI design are essential drivers in the ongoing journey of software modernization, enabling organizations to improve usability, productivity, and business outcomes through iterative assessment, collaborative development, and a focus on user needs.