As Large Language Models have been trained with massive amounts of data, they can provide impressively fluent answers. Unfortunately, the answers are not always correct. Passing in context to quest...

One of the most impressive features of Large Language Models (LLMs) is the ability to answer questions in fluent language. This post describes some of the underlaying techniques and how to avoid ha...

Foundation Models are a game change and a disruptor for many industries. Especially since ChatGPT has been released, people realize a new era of AI has begun. In this blog I share my experience lea...

Training Foundation Models is expensive. Techniques like Prompt Engineering address this by freezing the models and providing context in prompts to optimize results at the expense of losing perform...

Foundation Models are the foundation for different AI downstream tasks. To leverage these generic models for specific tasks, prompt engineering is a technique to optimize the results without having...

While there are several playgrounds to try Foundation Models, sometimes I prefer running everything locally during development and for early trial and error experimentations. This post explains how...

Training foundation models and even fine-tuning models for custom domains is expensive and requires lots of resources. To avoid changing the pretrained models, a new more resource-efficient techniq...

Generative foundation models like ChatGPT can handle fluent conversations pretty well. IBM Watson Assistant can be extended with NeuralSeek to search and provide answers from enterprise knowledge b...

Since I’m excited by the incredible capabilities which technologies like ChatGPT and Bard provide, I’m trying to understand better how they work. This post summarizes my current understanding about...

Watson Natural Language Processing Library for Embed is a containerized library designed to empower IBM partners with greater flexibility to infuse powerful natural language AI into their solutions...