Data & AI

The Progress Agentic RAG solution empowers OpenEdge users to unlock actionable, trustworthy AI insights by seamlessly combining structured and unstructured enterprise data, accelerating modernization, boosting productivity and enabling explainable, cost-effective AI adoption without disrupting existing operations.

Integrating the Progress Corticon.js solution and MarkLogic platform can bring decision intelligence directly to your data, enabling high-performance, transparent and explainable automation of complex decisions across various industries.

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.