Author Archives: dselz

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 →

Last Sunday, I delved into the often misconceived notion of a seemingly easy recipe for a Retrieval Augmented Generation (RAG) solution. Many seem to believe it’s as straightforward as combining a vector database, a sprinkle of LangChain, and a dash … Continue reading →

In the grand tradition of the tech industry, I currently see a number of folks jumping onto the latest bandwagon without a seatbelt. And a number of riders are about to fall into the trap of believing that the latest … Continue reading →

In today’s saturated marketplace, there is a cacophony of voices and solutions. Navigating this noise demands differentiation that delivers value. SquirroGPT offers exactly that. Here’s what sets the solution apart: Three Pillars of Excellence: At the core of the offering … Continue reading →

The Balance Between Chat Systems and Keyword Search In the realm of information access and retrieval, the surge in popularity of chat systems, particularly models like ChatGPT, has been nothing short of impressive. These systems, with their ability to understand … Continue reading →

The world of artificial intelligence is rife with innovations, and one of the most notable recent advancements is the Retrieval Augmented Large Language Model (raLLM). While it’s tempting to simplify raLLM as a mere amalgamation of a vector database, some … Continue reading →