Operations | Monitoring | ITSM | DevOps | Cloud

In a 2020 paper, Patrick Lewis and his research team introduced the term RAG, or retrieval-augmented generation. This technique enhances generative AI models by utilizing external knowledge sources such as documents and extensive databases. RAG addresses a gap in traditional Large Language Models (LLMs). While traditional models rely on static knowledge already contained within them, RAG incorporates current information that serves as a reliable source of truth for LLMs.

My name is Tim Gühnemann, and as an AI engineering working student at ilert, I had the privilege of developing and continuous improving ilert AI, ensuring it meets the needs of our customers and aligns with our vision. ‍ Our goal was to provide all our customers with access to ilert AI. We aimed to develop a solution that could adapt dynamically and function independently based on our use cases, similar to the OpenAI Assistant API.

As digital applications and infrastructures grow increasingly complex, managing and understanding log data has become increasingly vital in achieving practical observability, enabling organizations to detect, diagnose, and prevent issues across their systems. However, traditional log analysis methods often struggle with the volume and complexities of modern log data in cloud-native environments.

Artificial intelligence (AI) has been a part of technological innovation since the 1950s, but recent advances in large language models (LLMs) have accelerated its impact. These advancements have sparked widespread excitement, but many are missing the real disruption on the horizon: user experience (UX). As a company deeply rooted in UX innovation, we at InvGate are closely examining how this evolution will unfold.

The third article in our Data Economy series examines what it takes to unlock the opportunities with AI. Based on some of the key findings of our own research ‘Predicting the Data and AI Revolution’ and exclusive insights provided by leaders in the data economy, this piece explores how organizations are currently using AI.

Today’s IT teams face unprecedented challenges as they manage increasingly complex hybrid and multi-cloud environments and vast amounts of data. The pressure to maintain uptime, optimize performance, and ensure security – all while balancing limited resources – has become a daunting task for even the most seasoned professionals. So how can these organizations stay ahead of the curve?