Before we get into the specific tasks, I want to address the thing that puts people off AI-generated content most quickly: the generic tone.
You have seen it. Probably produced it. The email that is technically correct but reads like it could have come from any advisor anywhere or obviously from AI. The itinerary introduction that hits all the right notes but has no particular voice behind it. The social post that is fine but forgettable.
That is not an inherent quality of AI output. It is the output of an under-briefed prompt. And by this point in the course you have two things that fix it directly: a context document that has already told the tool who you are and how you write, and a prompting framework that tells it exactly what this specific task needs.
But there is a third element that matters enormously for content and communications work specifically, and it is this: the more specific the client and situational detail you put in, the less generic the output will be.
Generic input produces generic output. That is always true. But in content and communications it is especially visible, because the difference between a good client email and a great one is almost always the specific detail that makes the client feel seen rather than processed. AI can produce that specificity if you give it the raw material. It cannot invent it.
So the habit to build, before you prompt for any piece of client-facing content, is this: what do I know about this client, this situation, this moment that is specific enough to make this communication feel genuinely personal? Write that down first. Then build your prompt around it.
