Explore the latest insights shaping how teams build, train, and trust artificial intelligence in practical settings. This week's featured content highlights two essential sides of the AI lifecycle: creating effective training programs and rigorously evaluating the systems that power them.
In a video from OpenAI shared via YouTube, Austin Braham at Realpage demonstrates how ChatGPT Work transforms raw ideas, notes, and source materials into complete AI training packages tailored to different teams, roles, and skill levels. The session illustrates a practical workflow for moving from scattered information to structured, role-specific learning resources—an approach that can help organizations scale AI literacy without reinventing the wheel for every department.
Complementing that hands-on perspective, H2O.ai presents a video via YouTube on evaluating LLMs, retrieval-augmented generation (RAG) systems, and agents in practice. The core message is direct: you cannot trust what you do not measure. Evaluation is framed not as a one-time checkpoint at the end of development, but as an ongoing discipline for systematically measuring quality, accuracy, and reliability. Because evaluation looks different depending on whether the subject is a model, a retrieval system, or an agent, the discussion breaks down terms and approaches that help practitioners choose the right metrics and methods for each case.
Together,
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