Eugene Yan's recent post outlines a simple yet powerful framework for technical writing: the Why-What-How structure applied to three recurring documents. While not AI-specific, the approach maps directly to the needs of machine learning teams, where context and rationale often get lost in code and experiments.
The one-pager answers why a project exists, the design doc details what will be built and how, and the after-action review captures what happened and what to change. Yan argues that this consistent structure reduces friction when sharing work across teams and over time.
For AI/ML practitioners, the value is clear: model decisions, data choices, and failure modes become explicit. The source is a single perspective, so there is no contrasting view, but the framework's simplicity suggests it can be adapted to various team sizes and project stages without much overhead.