Iterative Prompt Refinement
Treat an AI answer as a negotiable draft. A specific demand for revision can raise the model’s stopping threshold and elicit stronger work than the first plausible response.
A student typed only “do better” after receiving an AI-written essay. The essay improved—not once, but three times in succession.
E1The first answer is a stopping point, not a ceiling
An AI can produce an acceptable response before exhausting what it can generate. A follow-up demand changes the task: the existing answer becomes a draft to evaluate and revise, raising the threshold for what counts as sufficient. Even crude feedback can trigger another pass through wording, structure, or substance. This is LLM Satisficing viewed from the user’s side: refinement works because the first plausible answer need not be the strongest available one.
E1Where it shows up
Three rounds of “do better”
Each repetition produced a better essay, showing that useful improvement can remain latent after the initial response and be drawn out through successive revision.
E1Revision is not verification
A response can become more polished without becoming more accurate. Iteration is evidence that the output can change, not proof that it has converged on the truth; claims still need independent checking.
Commission a second draft
Tomorrow, before using an AI response, request one targeted revision: name the missing quality—more depth, more creativity, or more options—and compare the new version with the first instead of automatically replacing it.
Episodes that teach this
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Can you Bribe ChatGPT? AI Psychology 101 - Future IQ
· explained at 3:00
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A student repeatedly typed 'do better' and got a better essay three times in a row.