As Generative Models Improve, People Adapt Their Prompts
A large online experiment (N = 1,893) compares DALL·E 2, DALL·E 3, and DALL·E 3 with automatic prompt revision on a 10‑attempt image replication task. DALL·E 3 improves outcomes not only because the model is better, but because people change how they prompt when the model is stronger.
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Headline effect: Relative to DALL·E 2, DALL·E 3 yields images closer to targets by ∆CoSim = 0.0164, about z = 0.19 SD, with the gap widening across attempts.
Behavior adapts to capability: Without knowing which model they used, DALL·E 3 participants wrote longer prompts (+24%, +6.9 words on average) that added descriptive content, and their prompts became more semantically similar to each other over iterations.
Decomposed gains: About half of the improvement is due to the model itself and about half to users’ adapted prompting. The ATE splits into a model effect of ∆CoSim ≈ 0.00841 (51%) and a prompting effect of ∆CoSim ≈ 0.00788 (48%).
Prompt revision caveat: Automatic LLM prompt revision helps over DALL·E 2, but it cuts the DALL·E 3 advantage by ~58% and can misalign with user goals.
Takeaway: As models advance, users naturally supply richer, more consistent prompts that the newer models can realize more effectively. Prompting remains central to unlocking capability gains.