Iterative prompting
Also called prompt iteration, refinement prompting, follow-up prompting
Improving an AI result through a series of follow-up instructions, rather than expecting a perfect answer first time.
Iterative prompting is the everyday practice of refining an output over several turns. You ask, look at the result, then give specific feedback: "shorter", "more formal", "use the second example instead", "you missed the deadline in paragraph two". Each turn builds on the previous output and everything said so far in the conversation.
It is often faster than writing a perfect prompt up front, especially for creative or subjective tasks where you only know what you want once you see a draft. The key is specific feedback. "Make it better" gives the model little to work with; "cut the opening paragraph and lead with the price change" gives it a clear action.
Iteration has two practical limits. Long conversations can drift, as early instructions get diluted or contradicted. And the improvements live only in that chat. When you find yourself giving the same corrections every time, fold them into the original prompt or a reusable template, so the next attempt starts where the last one finished.
Example
First draft of a product announcement is too long and too excited. Follow-up 1: "Half the length, remove adjectives." Follow-up 2: "Open with the customer problem, not the feature name." Follow-up 3: "Add the launch date and price in the second sentence." Three short turns produce a usable draft.
Common mistake
Iterating forever in one conversation. After many rounds, the model may start contradicting earlier fixes. If quality starts slipping, start a new chat with a prompt that includes everything you learned, plus the best draft so far.