Businesses invest heavily in better and more powerful generative AI systems. It is a common assumption, superior models will automatically lead to superior results. However, new research from affiliates of the MIT Sloan School of Management suggests that model advances are only half of the equation. In a large-scale experiment, researchers found that the other half of performance gains comes directly from how users adapt their prompts to take advantage of a new system.
How user adaptation drives half of performance gains
To understand the interplay between model quality and user skill, the researchers conducted an experiment with nearly 1,900 participants using OpenAI’s DALL-E image generation system. Participants were randomly assigned to one of three groups: one using DALL-E 2, a second using the more advanced DALL-E 3, and a third using DALL-E 3 with their prompts secretly rewritten by the GPT-4 language model. Each person was shown a reference image and given 25 minutes to re-create it by writing descriptive prompts, with a financial bonus offered to the top 20% of performers to motivate improvement.
The study revealed that while the DALL-E 3 group produced images that were significantly more similar to the target image than the DALL-E 2 group, this was not just because the model was better. The researchers found that roughly half of this improvement was attributable to the model upgrade itself, while the other half came from users changing their behavior. Users working with the more advanced DALL-E 3 wrote prompts that were 24% longer and contained more descriptive words than those using DALL-E 2. This demonstrates that users intuitively learn to provide better instructions to a more capable system.
Crucially, the ability to write effective prompts was not limited to technical users. The study’s participants came from a wide range of jobs, education levels, and age groups, yet even those without technical backgrounds were able to improve their prompting and harness the new model’s capabilities. The findings suggest that effective prompting is more about clear communication in natural language than it is about coding. The research also found that these AI advances can help reduce inequality in output, as users who started at lower performance levels benefited the most from the improved model.
The surprising failure of automated assistance
One of the most counterintuitive findings came from the group whose prompts were automatically rewritten by GPT-4. This feature, designed to help users, actually backfired and degraded performance in the image-matching task by 58% compared to the baseline DALL-E 3 group. The researchers discovered that the automated rewrites often added unnecessary details or misinterpreted the user’s original intent, causing the AI to generate the wrong kind of image. This highlights how hidden, hard-coded instructions in an AI tool can conflict with a user’s goals and break down the collaborative process.
Based on these findings, the researchers concluded that for businesses to unlock the full value of generative AI, they must look beyond just acquiring the latest technology. The study offers several priorities for leaders aiming to make these systems more effective in real-world settings.
- Invest in training and experimentation. Technical upgrades alone are not enough to realize full performance gains. Organizations must give employees the time and support to learn and refine how they interact with AI systems.
- Design for iteration. The research showed that users improve by testing and revising their instructions. Therefore, interfaces that encourage this iterative process and clearly display results help drive better outcomes.
- Be cautious with automation. Automated features like prompt rewriting can hinder performance if they obscure or override what the user is trying to achieve.
