Author Archives: dselz

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 →

Large Language Models (LLMs) have taken the world of artificial intelligence by storm, showcasing impressive capabilities in text comprehension and generation. However, as with any technology, it’s essential to understand its strengths and limitations. When it comes to search functionality, … Continue reading →

In the ever-evolving landscape of artificial intelligence, the emergence of Retrieval Augmented LLMs (raLLM) has marked a significant turning point. This innovative approach, which combines an information retrieval stack with large language models (LLM), has rapidly become the dominant design … Continue reading →

The other day we were asked to take part in a accelerator program. As always some form to be filled out. I was short on time. In fact it was aleady past the official deadline. But the organizer wanted us … Continue reading →

We released GPT for the Enterprise – https://squirro.com/enterprise-generative-ai-and-large-language-models/… Obviously we’re trying it on our own stuff, e.g. our ISO Rulebook. And as obvious we try to see if we can break it. Here’s a test. We failed… You can try … Continue reading →