Iterative refinement means asking a question, reviewing the AI's response for usefulness and accuracy, then asking targeted follow-ups that improve the answer in the next round. You're not trying to be perfectly clear the first time; instead, you use each exchange to understand what the model needs and to steer it closer to your goal.
Iterative refinement is the practice of treating the first AI response as a draft, then progressively improving it through targeted follow-up instructions rather than starting over from scratch.
This technique saves significant time and produces higher-quality results because each round narrows the gap between what AI generates and exactly what you need.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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