Spot the policy traps
Spot the policy traps
A teammate drafted a summary for the team wiki: “What happens to our ChatGPT data.” You get one review pass — it will shape what colleagues paste into chats, so every claim has to be right.
Flag every claim that misstates what the provider's controls actually do. Leave the accurate ones.
The AI answer
Here is our summary of what happens to our ChatGPT data. When you turn off model training in your personal account, new conversations stop being used to train OpenAI models, and your saved chats are automatically deleted at the same time. Archiving an old chat removes it from training eligibility. Temporary chats are excluded from history and memories and are never retained, though they can still be personalized if you choose. Content in Business and Enterprise workspaces is not used for training by default. Memory settings and model-training settings are the same control, so turning off memory also stops training. Finally, if you rate a response with a thumbs up or down, the whole conversation may be used for training even if you opted out.
Hint
Compare each claim against what a control toggles: future use, stored history, or neither?
Watch for absolutes (“never”, “automatically”, “same control”) — policy pages almost never grant them.
Why this is the answer
Three traps and three truths: turning training off does not delete saved chats, archiving does not change training eligibility, and temporary chats can be retained up to 30 days for safety — while the managed-workspace default and the feedback override are accurate and worth keeping. Source: OpenAI's data-controls page, checked 2026-09-23. The wider lesson is the shape of the exercise: policy claims need a source, a date, and a precise verb — “stops training future conversations” is not the same as “deletes history”.