Category: Artificial Intelligence (AI) and LLM
Two weeks ago I went to PyData Amsterdam which kicked off with a tutorial day. These tutorials included a workshop on creating multi-modal LLM agents, advanced web-scraping techniques and …
Explore key lessons in implementing a Retrieval-Augmented Generation (RAG) system, balancing innovation with practicality for enhanced AI responses.
Running Large Language Models (LLMs) locally offers enhanced privacy, independence, cost effectiveness, and unrestricted use. This guide covers tools like LM Studio and Ollama for setup and hands-on learning.
Can the power of GPT-4 be leveraged to build a high-performance API? In this article we will instruct GPT-4 to build a high-performance API in GoLang using Protocol Buffers …
At the beginning of this month, at AMIS, I attended a Special Interest Group (SIG) meeting about GitHub Copilot, given by one of my colleagues. As we do so …
In the rapidly evolving world of artificial intelligence, language models stand at the forefront of technological innovation and ethical debate. Among these, OpenAI’s suite of models, including the renowned …
With the use of ChatGPT, anyone has access to vast domain knowledge. Even a generalist like me (my colleague would call me a dummy) can act as an expert …
ChatGPT is a bit of a “tongue twister” so I will speak of Cheppy. AMIS has a long history of spotting, exploring, embracing and rolling out new concepts and …
