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Opting out of AI training usually stops a defined category of future prompts or other content from being used to improve a provider’s models. It does not automatically delete chat history, erase account data, change every retention period, or remove information from models that were trained earlier. The exact result depends on the product, account type, setting and exceptions in that provider’s policy.
Opting out is not the same as deleting your data
“Do not train on my content” describes a use restriction, not a universal deletion command. A provider can stop using eligible new submissions for model improvement while retaining conversations in your account history, keeping records for security or legal reasons, or applying separate rules to feedback and connected features.
Keep these controls separate when you evaluate what happened to your information:
- Training or model improvement: whether prompts, responses, files, ratings or other content may be used to develop or improve models.
- Chat history: whether conversations remain visible in your account and synchronized across devices.
- Retention: how long the provider keeps content or metadata, including records needed for abuse prevention, security, billing or compliance.
- Deletion: an action that removes eligible data under the provider’s deletion process; it is different from changing a training preference.
- Human or automated review: safety and abuse systems may inspect content under separate terms, even when training use is disabled.
Therefore, if your goal is to remove a conversation, use the product’s delete or account-deletion controls as well as changing the training setting. If your goal is to prevent future model-improvement use while keeping history, look for a prospective training toggle and verify what it covers.
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What the setting usually changes
A training opt-out normally applies to submissions made after the control takes effect, according to the provider’s documented scope. The setting may be account-wide, limited to one conversation mode, or available only for certain plans. It can cover prompts and responses without covering thumbs-up feedback, uploaded files, integrations or information sent to a separate tool.
Do not assume that opting out triggers “unlearning.” The available provider documentation does not establish a universal process for removing information from models that were already trained. The defensible interpretation is narrower: the provider says eligible future content will not be used for the specified improvement purpose, subject to its stated exceptions.
Safety monitoring, abuse investigations, legal obligations and operational security can have their own retention or review rules. A setting that limits training does not promise that no employee or automated system can ever access a submission.
ChatGPT: personal settings and separate product rules
Turn off model improvement for a personal account
OpenAI’s Data Controls documentation identifies two routes for personal ChatGPT users:
- Open ChatGPT and go to Settings → Data Controls.
- Turn off Improve the model for everyone.
- Alternatively, use OpenAI’s privacy portal and choose Do not train on my content.
The control concerns model improvement. OpenAI describes chat history and other data controls separately, so turning off the setting should not be treated as a command to erase conversations already stored in the account. To remove a conversation, use the chat’s delete action; to request broader account-data deletion, follow the applicable account or privacy-portal process.
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What happens to chats after the change?
OpenAI’s documentation does not say that the model-improvement toggle automatically clears existing history. A chat can remain in the account even when its content is excluded from the defined training use. Check the deletion and retention terms for the product and region that apply to you, and make a separate deletion request when removal is the objective.
Business, Enterprise, Edu and API accounts
OpenAI states that inputs and outputs from ChatGPT Business, Enterprise, Edu and the API are not used to train models by default. That is a product-policy distinction, not a reason to assume that every paid or organizational account has identical retention, administrator access, safety-review or deletion behavior. Review the terms for the exact workspace or API arrangement.
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Codex has an additional Include environments control that determines whether full environment context may be used for training. This is separate from the general ChatGPT setting. If an environment contains source code, secrets or customer data, verify this control before sending context and apply your organization’s data-handling rules.
Claude: consumer Incognito chats and API retention
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Anthropic’s Privacy Center says that Incognito chats are not used to improve Claude, even when the main Model Improvement setting is enabled. Incognito is therefore a conversation-level option with a different training treatment from the account’s general preference.
That statement addresses improvement use; it is not a promise that the conversation is instantly erased from every system. Read the consumer product’s history, deletion, safety and retention information separately if you need a specific deletion outcome.
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Commercial and API customers
Anthropic separates consumer guidance from its commercial and API terms. Its API retention documentation says retained data is not used for model training without express permission. The same documentation describes different retention arrangements for cloud-provider platforms and records required for compliance, so an API customer should identify the deployment and contract before drawing conclusions about storage duration.
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Gemini API: what the verified documentation does—and does not—say
The Google materials covered here concern the Gemini API, not the consumer Gemini application. They describe developer choices about logging requests and responses and about contributing datasets for product improvement and training. A developer must check the project’s configuration and the applicable service terms rather than infer behavior from a consumer-app activity switch.
Zero data retention (ZDR)
Google’s Gemini API documentation describes approved ZDR projects in which user content and identifiable metadata are cleared before logging. ZDR is not an unconditional guarantee that no data is ever held: feature-specific exceptions apply.
The 30-day Search grounding exception
For Search grounding, prompts, contextual information and generated output may be stored for 30 days for grounding and search-suggestion purposes. This is a documented Gemini API policy period, not a general statistic and not evidence about the consumer Gemini app.
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Because the available sources do not verify the consumer app’s current training or activity controls, do not tell consumer Gemini users that disabling an activity setting produces the API’s ZDR result.
Compare the controls on five separate axes
A “private” or “not private” label hides important differences. Use the following checklist for any provider and product:
| Axis | Questions to answer | Why it matters |
|---|---|---|
| Product and account | Is it a personal account, business or enterprise workspace, education plan, API project or developer platform? | Policies and default training treatment can change between products. |
| Training scope | Does the control cover prompts, responses, files, feedback, environment context and connected tools? Is it prospective? | An opt-out may cover only a defined subset of submissions. |
| Storage and deletion | Do chats stay in history? What action deletes them, and what retention period or legal hold can apply? | Training exclusion does not establish erasure. |
| Exceptions | Are safety review, abuse monitoring, legal duties, grounding or human evaluation treated separately? | Exceptions can permit limited access or storage after an opt-out. |
| Control clarity | Where is the setting, and is it account-wide, conversation-specific or project-specific? | You need to change the control that actually governs the data you send. |
How to verify that your opt-out did what you intended
- Identify the exact product. Record whether you are using a personal chat app, an organizational workspace, an API key, a cloud-hosted deployment or a developer feature.
- Read the setting label literally. Note whether it says “model improvement,” “training,” “history,” “logging” or “activity.” Similar words can control different systems.
- Check the effective scope. Look for language about future submissions, feedback, files, temporary or Incognito chats, connected tools and administrator access.
- Change the control before sending sensitive material. A later opt-out should not be treated as a guaranteed retroactive withdrawal of content already submitted.
- Handle existing conversations separately. Delete individual chats or use the provider’s account or privacy request process if removal is required.
- Confirm special features. For APIs, inspect project logging, dataset-contribution and grounding settings. For Codex, inspect Include environments. For Claude, choose Incognito when that conversation-level treatment is appropriate.
- Keep a record. Save the date, product, workspace and setting state so you can explain which policy was in effect when a submission was made.
Common assumptions that are wrong
“Opting out deletes my chats.”
Not by itself. Training use and deletion are separate controls. A conversation can remain in history or another retention system unless you delete it under the provider’s process.
“Opting out removes my data from existing models.”
No universal retroactive untraining promise is established by the provider documents described here. Treat the setting as a forward-looking restriction unless the provider explicitly documents another remedy.
“A business plan automatically means zero retention.”
Business, enterprise, education and API products may exclude content from training by default, but that does not settle retention, safety review, administrator visibility, cloud-platform handling or legal records. Read the terms for the exact plan.
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“A temporary, Incognito or ZDR mode means no system can see the data.”
These modes can change improvement or logging treatment while leaving safety, abuse, grounding or compliance exceptions in place. ZDR in the Gemini API also has the documented Search grounding exception.
“One provider’s setting works the same everywhere.”
It may not. OpenAI’s personal ChatGPT toggle, its organizational defaults and Codex’s environment control address different products. Anthropic’s consumer Incognito chats and API retention terms are also distinct. Google’s verified ZDR material applies to the Gemini API, not automatically to the consumer app.
What to do before sharing sensitive information
- Use the least sensitive data needed to accomplish the task; remove names, account numbers, credentials and unnecessary customer details.
- Prefer an organizational or API configuration whose written terms match your retention and training requirements.
- Disable training or improvement before submitting content, rather than relying on a later request.
- Do not paste secrets into a prompt merely because a training opt-out is enabled; security and access risks remain.
- Check whether files, plugins, connected tools, search grounding or environment context follow separate policies.
- Delete stored conversations when your retention policy requires it, and document any exception that prevents immediate deletion.
Bottom line for the three documented cases
For personal ChatGPT, turning off Improve the model for everyone or selecting Do not train on my content limits the documented model-improvement use; it does not replace chat deletion. ChatGPT Business, Enterprise, Edu and API inputs and outputs are excluded from training by default according to OpenAI, while Codex has a separate environment-context control.
For consumer Claude, Anthropic says Incognito chats are not used to improve Claude. Its API documentation separately addresses retained data and says it is not used for training without express permission, with deployment-specific retention arrangements.
For the Gemini API, developer logging, dataset contribution and approved ZDR settings govern the result, with a 30-day Search grounding exception. Those API rules should not be presented as the behavior of the consumer Gemini app. In every case, verify the exact product, scope, retention terms and exceptions before deciding that your data has been deleted or made inaccessible.
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