OpenAI Rebuilds ChatGPT's Model Spec Around Five Principles

OpenAI has updated the Model Spec, the document that sets out how it wants its AI models to behave when talking to users and carrying out tasks. The company has reorganized the text around five principles: understanding user intent, limiting harm, honesty, supporting human decision-making, and considering the people affected by a model's work.

According to a statement on the company's official website, the update adds guidance on reading the context of a conversation, using information tied to the user, and respecting human oversight. It also revises some rules on how models respond, and it drops most of the earlier detailed instructions on writing style.

What the five principles cover

The first principle asks models to work out what a user is trying to achieve rather than execute their words literally and ignore context. The second concerns avoiding potential harm from answers or from actions the model takes.

The third centers on honesty and transparency about what the system knows and what it cannot confirm. The fourth aims to support the user's ability to think and choose for themselves. The fifth asks the model to consider the circumstances of people who may be affected by the results and to help users produce work they can rely on.

Not a new model, and not fully implemented

The update is not a new model launch, and it does not mean every change is already live in ChatGPT. OpenAI said the document describes the behavior it is working toward, and that its current models do not yet fully reflect all of the new principles.

The company says it is developing training methods that help models understand the reasons behind instructions, not just follow them. It also wants writing style to respond more closely to a user's preferences, language and work context, while safety limits and other controls continue to apply.

The shift signals a move away from rigid, rule-by-rule instructions toward judgment in situations where direct instructions alone fall short. For users, the practical effect will depend on how quickly those training methods show up in the models they use.