Getting good output from an AI writing tool has less to do with the tool and more to do with the prompt. The same model can produce generic filler or genuinely useful drafts depending on how specific you are about what you want.
Start with a role, not just a task. Instead of “write a blog intro,” try “write a blog intro as a personal finance blogger explaining a concept to someone who’s never budgeted before.” Giving the AI a role narrows its tone and vocabulary before it writes a single word.
Say what format you want, explicitly. “List 5 tips” produces something very different from “write a 3-paragraph explanation.” AI models default to whatever’s statistically common for the topic — if you don’t specify, you’ll often get a generic bulleted list even when a narrative would serve better.
Give it a constraint to work against. Open-ended prompts get open-ended (often bland) answers. Adding a constraint — a word limit, a target reading level, “avoid jargon,” “keep it under 100 words” — forces sharper output.
Iterate instead of restarting. If the first draft is close but not right, tell the AI specifically what to change (“make the tone more casual,” “cut the second paragraph,” “add a concrete example”) rather than rewriting the whole prompt from scratch. Models handle targeted revision better than most people expect.
Keep a human editing pass. AI-drafted content still needs a real read-through for accuracy, tone consistency, and anything that sounds plausible but isn’t actually true — a known failure mode of language models. Treat AI output as a first draft, not a finished one.
If you want to try this without writing prompts from scratch, our Advanced AI Prompt Generator builds role/task/format/constraint combinations for you across several categories — a useful starting point to adapt rather than a replacement for your own editing judgment.