As Generative Models Improve, People Adapt Their Prompts

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.