Model behavior, or the way that models respond to input from users—encompassing tone, personality, response length, and more—is critical to the way humans interact with AI capabilities. Shaping this behavior is a still nascent science, as models are not explicitly programmed but instead learn from a broad range of data⁠.

Shaping model behavior must also take into account a wide range of questions, considerations, and nuances, often weighing differences of opinions. Even if a model is intended to be broadly beneficial and helpful to users, these intentions may conflict in practice. For example, a security company may want to generate phishing emails as synthetic data to train and develop classifiers that will protect their customers, but this same functionality is harmful if used by scammers.

We’re sharing a first draft of the Model Spec⁠(opens in a new window), a new document that specifies our approach to shaping desired model behavior and how we evaluate tradeoffs when conflicts arise. It brings together documentation used at OpenAI today, our experience and ongoing research in designing model behavior, and more recent work, including inputs from domain experts, that guides the development of future models. It is not exhaustive, and we expect it to change over time. The approach includes:

1. Objectives: Broad, general principles that provide a directional sense of the desired behavior