Act One: From Chatbots to AI Agents

This eMag includes:

• "Prompt Engineering" by Hien Luu highlights the parallels between prompt engineering and programming, emphasizing the structured approach necessary for effective language model interaction. It argues that, despite AI advancements, prompt engineering remains a crucial skill for bridging human intent and AI capability.
• "A Framework for Building Micro Metrics for LLM System Evaluation" by Denys Linkov outlines a practical framework for evaluating LLM systems, stressing the need to align metrics with business goals. It advocates for an iterative "crawl, walk, run" approach to development, highlighting the importance of robust evaluation as AI agents integrate into critical workflows.
• "Elevate Developer Experience with Generative AI Capabilities on AWS" by Olalekan Elesin demonstrates how AWS tools like Amazon Bedrock enhance development, improve code, and boost productivity. It provides guidance on integrating generative AI, addressing adoption concerns, and emphasizing security, cost, and human oversight in the evolving AI-driven development landscape.
• "Eclipse LMOS: Launching AI Agents across Europe at Breakneck Speed" by Arun Joseph and Patrick Whelan describes the development of LMOS, a multi-agent platform for deploying LLM applications in distributed environments. It highlights the technical challenges overcome, accelerated deployment, and the democratization of agent development through open source, showcasing the impact of innovation and collaboration.
• "Navigating Responsible AI in the FinTech Landscape" by Lexy Kassan stresses the need for ethical AI principles and policies in finance, addressing the evolving regulatory landscape. It advocates for cross-functional collaboration and AI governance processes, emphasizing responsible development for trust and societal impact.

As you read this collection of articles, we encourage you to reflect on how these resonate with your experiences, challenge your perspectives, and inspire your future roadmap. We aim for this emag to become a guide and a catalyst for your continuous learning and adaptation in the ever-changing realm of technology.

Thank you for reading! And, as always, please send any feedback to us at editors@infoq.com

This content is in the AI, ML & Data Engineering topic