Transformer based AI models can generate amazing answers to users’ questions. While the underlaying Large Language Models are not retrained, the performance of Question Answering AI can be improved...

Large Language Models can improve search results significantly, since they don’t try to find exact word matches but passages of text that fit best to the questions. This post explains high level in...

Large Language Models can improve the user experience of virtual assistants like Watson Assistant by providing answers rather than lists of links. With Watson Assistant’s ‘Bring your own Search’ fe...

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...