Author Archives: dselz

A year ago, Credit Suisse went under. The rescue required 1/3 of Switzerland’s gross national product. CS was about a third as big as UBS. In other words, a new rescue (and most G-SiBs have come knocking in the recent … Continue reading →

RAG wasn’t a concept 9 months ago. Today everybody talks about how to ‘Chat with Data’ and to do that us and others have popularized the concept of Retrieval Augmented Generation, RAG in short. The traditional RAG stack combines the … Continue reading →

Yes, AI will displaces jobs. And it will provide opportunities for growth. Here’s a smart way to think about it. 25 years ago, when I had notably fewer gray hairs, I was part of a team that launched one of … Continue reading →

At the start of this is a simple and yet perennial CEO question: “My gross-margin got hammered. Why did my gross margin decline, and what actions should I take?” The complexity of modern enterprises means the answer is seldom straightforward, … Continue reading →

At its core, automation enables systems, operations, or mechanisms to function independently without direct human intervention. Pursuing this concept has shaped technologies, transformed industries and economies, and left a significant mark on the labor force. From the early gears of … Continue reading →

Last year Large Language Models (LLMs) took center stage, signaling a new era in technology and innovation. This transformative wave wasn’t just a blip on the radar for tech aficionados; it became a pivotal focus for Chief Information Officers and … Continue reading →

As we navigate the ever-evolving AI landscape, bold predictions about its immediate and long-term impacts have become increasingly commonplace. The start of the year, in particular, brings forth a flurry of these prediction posts. While these forecasts often swing between … Continue reading →

Welcome to another year of transformative change and surprising stabilization and consolidation Following the pattern of massive change in 2022 and the linear progression of these changes in 2023, the year 2024 is likely to be characterized by both transformative … Continue reading →

In previous discussions, we extensively explored the multifaceted world of Retrieval-Augmented Generation (RAG) – a paradigm that synergistically combines the prowess of information retrieval with natural language generation to produce more informed and contextually rich responses. The series delved deep … Continue reading →

It has been claimed, that to create the ultimate Retrieval Augmented Generation (RAG) Stack, all one needs is a plain vector database, a touch of LangChain, and a smattering of OpenAI. Here are the 10 essential considerations when delving into … Continue reading →