This week’s updates spotlight two essential sides of the AI conversation: building practical training programs and measuring whether AI systems actually work. Together, the featured posts from OpenAI and H2O.ai offer a useful snapshot of how organizations are moving from experimentation to disciplined implementation.
In a video shared by OpenAI via YouTube, Austin Braham at Realpage demonstrates how ChatGPT Work can transform ideas, notes, and source materials into complete AI training packages tailored to different teams, roles, and skill levels. The session highlights a growing priority for businesses: equipping employees with role-specific AI knowledge rather than one-size-fits-all guidance. By turning raw inputs into structured learning content, the approach shows how AI can support internal enablement at scale.
On the evaluation side, H2O.ai presents a practical look at assessing LLMs, retrieval-augmented generation systems, and agents. The core message is direct: you cannot trust what you do not measure. The video breaks down evaluation as the systematic measurement of quality, accuracy, and reliability—not a one-time checkpoint at the end of development, but an ongoing practice woven throughout the lifecycle. It also distinguishes how evaluation differs depending on whether the target is a model, a retrieval system, or an agent.
Read together, the
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- The Open Source AI Paradox: Control, Trust, and the Future of InnovationThe Open Source AI Paradox: Why Openness Is Both the Solution and the Problem Open source AI is often framed as a battle between open and closed systems, but a closer look reveals a deeper paradox: the same openness that drives … Read more
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