OpenAI's new guidance: keep your AI's actions easy to check

Make the boundary clear before you begin

Before asking AI to work on a real task, write down what it may change and what you want to review. A clear boundary helps you judge the result, whether you are practising, building an app or buying one.

On October 29, OpenAI updated its Model Spec, the public document describing how it wants its AI models to behave. The revision puts more emphasis on understanding people’s intentions, keeping people able to choose and explaining the effects of actions.

This is a guide to intended behaviour. OpenAI says its models do not yet fully reflect it. Treat it as a useful basis for questions and checks when you use or build an AI tool.

Read the announcement: OpenAI: Updated Model Spec announcement.

A clearer view of the behaviour to expect

The document is now organised around five values: following human intent, reducing harm, being honest and transparent, supporting people’s ability to make their own choices, and caring about those affected.

The update adds guidance on how past conversations and saved preferences should inform a response. It also expands guidance on oversight and on controlling and explaining effects beyond the immediate task.

For a builder, that raises a practical question: can someone see what the tool understood and correct it? A customer’s old preference may help with a new request, but it may also be out of date. Give people a clear way to change an assumption before it shapes the work.

Design the moment when someone checks

Consider a tool that prepares changes to a list of customer requests. A useful first version could show the proposed changes, explain any missing information and let the person correct the draft before saving it.

That example is our suggested design exercise, not a feature announced by OpenAI. The point is to make the tool’s understanding visible while the choice is still easy to change.

Agree the review point with the person who owns the work. Updating a private draft and sending information to someone else can have very different consequences. Your app should make that difference clear.

Practise with one small example

Use made-up information while you learn. You do not need a complex app to practise giving a clear instruction and checking whether it was understood.

1. Choose one task and say what a good result should look like.
2. Write what the AI may change and when it must ask you first.
3. Include an incomplete entry or an old preference and check how it handles the uncertainty.
4. Correct the draft, then check that the final result follows your correction.

Keep building your own judgement

Our perspective is that the ability to explain, review and correct AI work will remain useful as tools gain more capabilities. Ask for a result you can understand, then inspect it. In a guided build or training session, make that review part of the lesson and part of the product.