Our Thinking
Short, practical observations from building and refining the course curriculum. Not a blog in the traditional sense, more a running record of what we keep noticing.
A vague prompt gives a model too much room to guess at tone, structure, and audience. The fix isn't a longer prompt, it's a more structured one. Context, constraints, and format each do specific work, and skipping any of them shows up in the output. We walk through this distinction in the first prompting module.
It's rarely the sentence-level grammar. It's confident-sounding claims that aren't sourced, subtle shifts in tone partway through a piece, or statistics that sound plausible but weren't verified. A useful checkpoint targets these specific failure patterns instead of just proofreading generally.
Give ten writers the same brief without a shared reference, and you'll see ten different voices too. AI just makes the variation more visible, faster, because output arrives in bulk. The solution is the same one editorial teams have always used: a documented style reference everyone checks against.
AI tools are useful for surfacing what a topic generally covers and where the obvious gaps are. They are not a substitute for checking a primary source before a specific fact goes into a published piece. Teams that skip this step tend to find out about it after publishing, not before.
AI tends to amplify whatever process already exists. Teams with a clear brief-to-publish workflow tend to fold AI in cleanly, adding speed at specific points. Teams without a defined process tend to end up with faster chaos instead of faster output. The course assumes a process exists and helps you formalize where AI fits inside it.
These observations are expanded into full lessons across the six modules.
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