Design Principles for Generative AI Applications

Generative AI technologies have reached an inflection point in consumer adoption and enterprise value, sparked by technological advancements in machine learning architectures such as GANs [56,79], VAEs [86], and transformers [38,170].

Models such as StyleGAN [79], GPT [20,130,137,138], and Codex [28] have demonstrated that powerful generative models can produce works at a human-like level of fidelity. Today, consumer applications such as ChatGPT 1 , DreamStudio 2 , and DALL-E 3 are making these technologies widely available and setting the bar for people's expectations of what generative AI can do. Startups such as Cohere 4 and Anthropic 5 are reducing the friction of embedding large language models in consumer applications. Enterprises such as IBM, Microsoft, Amazon, and Google are creating platforms for businesses to infuse generative technologies into their products and services. This commercialization of generative AI technologies is fueled by the ultra-rapid development of large-scale foundation models [19] that reduce the time and costs for developing generative AI systems. However, much attention in machine learning research communities has focused on developing advancements to the technology: scaling model parameter counts [91,158], evaluating model performance [97,163,194], tuning models efficiently to perform new tasks [27,178], and aligning models [132,196] to reduce their propensity to produce speech that is hateful, abusive, profane, or otherwise toxic [60,70]. Although these advancements serve to improve the state of the art, they do not recognize an important half of what Ehsan et al. [43] call the "human-AI assemblage" -the human.

Generative models have enabled a radically new way for people to interact with computing technologies. People are now able to craft specifications for the kinds of outputs they desire, such as via natural language prompts, and generative models are able to produce outputs that conform to those specifications. Nielsen [127] recently identified this form of interaction as intent-based outcome specification and argued that it is the first new UI interaction paradigm in 60 years. This form of interaction is fundamentally different from previous interaction paradigms (e.g. punchcards, command line interfaces, and graphical user interfaces), because it shifts control over how computation is performed away from the user and toward generative AI models. With this shift in control, how are we to design user experiences that help people interact with generative AI applications in effective and safe ways?

Over at least the past four decades, researchers and practitioners within human-computer interaction (HCI) have produced numerous guidelines, principles, practices, and frameworks for the design of effective and safe computing systems. Some guidelines are presented as generally applicable to most kinds of interactive computing systems, such as Nielsen and Molich's heuristics [128] and Shneiderman et al.'s strategies for designing effective human-computer interaction [155]. Other design guidelines are technology-specific, such as Bevan's guidelines for web usability [16] and