Not ending with a single answer.
AI's first answer is not a finished product but a starting point. I understand the method of getting closer to better answers through repeated cycles of reviewing, asking again, evaluating, and revising.
The First Answer from AI is Just a Draft
When you first get an answer from AI, it's easy to think that's the best it can be.
If the result falls short of expectations, you tend to refine the prompt more carefully and start over from scratch. It feels like the only way. But there's a more natural approach.
Looking at the answer you got and asking again. Finding what's missing, making a specific request, and then looking at the result. This repetition is a more powerful approach than you'd think.
The "react" mentioned here is not a JavaScript framework. It refers to the cycle where you react to the AI's output, and the AI responds to that reaction in turn.
The First Answer is Close to a Draft
When you ask AI for something, the first answer is usually decent.
The direction is right but lacks depth, the format is good but the content is bland, or it goes in a slightly different angle from what you wanted. It's not completely wrong—it just hasn't fully reflected what you actually want.
This isn't a failure of the model. In most cases, the initial prompt I threw didn't contain everything I wanted. The AI answered based on what I gave it. So the first answer isn't a finished product but rather material to make what I actually want more clear.
When this perspective changes, the approach changes too.
Looking and Asking Again
The key is to create your next request based on the result when you receive the first answer.
For example, say you requested a draft article. The first answer came. The flow is decent, but the introduction is ordinary and the middle examples are abstract. From this point, there are two types of requests you can make:
# Vague revision request
"Make it better"
"Make it more sharp"
# Specific revision request
"Change the first paragraph of the introduction to start from a situation the reader has already experienced"
"Replace the example in the third paragraph with concrete numbers or cases instead of abstract explanation"
The first delegates interpretation to the AI. The second specifies the direction based on what I've observed. Though both are "revision requests," the quality of results differs.
Looking and asking again means reading the result and extracting the next question from it.
Setting Evaluation Criteria First
For iteration to be effective, you need to first establish what criteria you'll use to evaluate the answer.
Without criteria, judging whether something is "good or bad" becomes vague feedback. You have the feeling "something seems to be missing," but you don't know exactly where the problem is. Then your revision requests also become muddled.
Simple criteria are sufficient.
Things to check when evaluating this answer:
1. Is the core argument in the first paragraph?
2. Are examples specific? (numbers, names, situations)
3. Does the last sentence prompt the next action?
You can give these criteria directly to the AI. If you say "Evaluate this piece against the above criteria and find what's lacking," the AI will review its own answer and propose directions for improvement.
Having it evaluate its own answer. This is one way to speed up the iteration.
Revision Requests Must Be Specific
There's a frequent breaking point in the iteration process. When the revision request is too broad.
"Rewrite the whole thing" is no different from the first request. From the AI's perspective, there's no criteria, so it might write something similar to the previous answer or go off in a different direction entirely.
The narrower, the better. Pinpoint the part that needs fixing, explain what the problem is, and specify the desired direction concretely. If you pack three or more things into one revision request, the AI may not reflect everything, or it might fix less important things first. Focusing on one thing at a time is faster.
The goal is to capture scope, problem, and direction in one sentence, like "The second section only has generic explanations. Write one actual usage example for me."
Conversation Deepens When Repeated
First, the structure is set. Next, content is filled in. Then, expression becomes refined.
This order feels natural. If you try to complete everything from direction to expression all at once, you have to start over if the direction is even slightly off. If you check each step and move to the next, the distance you have to backtrack is short.
However, repetition itself is not the goal. Each repetition only matters when you look at the previous answer and extract a specific direction from it. Repeating the same request or asking again without evaluation is not iteration—it's spinning your wheels.
The AI's answer is not a finished product. It's material for making the next question.