Data & AI

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.

An AI agent refers to a software entity that performs automated tasks on behalf of humans or other systems. These agents are programmed to make decisions and take actions based on their environment and predefined goals. In the context of AI and machine learning, agents often leverage algorithms to analyze data, learn from outcomes and improve their performance over time, often more efficiently than a human could.

This is the third and final article in our series following DataVault’s implementation of Progress' RAG-as-a-Service platform, Progress Agentic RAG.

This article explores how to built sophisticated data pipelines, connect multiple sources and create AI-powered systems that transform scattered information into actionable intelligence—all while facing a critical compliance deadline.