How Do Chatbots Use the Information We Enter?

How do chatbots use the information we enter? They process your message and may use conversation context, files, account details or connected-app data to prepare a reply. This is how chatbots use the information we enter to respond: they use the information made available to the system to interpret the request and generate an answer. What happens after that depends on the chatbot’s design, provider, account type and settings: information may be retained, reviewed, shared with service providers or used for model improvement. Check those practices separately from how the chatbot generates an answer.

This guide gives you a practical workflow for checking what a chatbot receives, what it may do with that information, and what to do if something sensitive gets entered. The aim is to finish with a clear decision: what your team can safely use the chatbot for, what must stay out of prompts and uploads, and who to contact if there is a problem.

How do chatbots use the information we enter?

A chatbot may receive more than the latest message you type. Depending on its design, the request can include earlier conversation messages, account or authentication details, uploaded content, model settings, and information retrieved from connected tools; an application may then send relevant parts to a model to generate a response.

Here is a useful way to trace the flow:

You provide an input. This could be a question, a follow-up that relies on earlier messages, or an uploaded file. A customer-support chatbot may also be given information from a company knowledge base or an account system. Do not assume every chatbot has access to every type of information: access depends on the specific product and configuration.
The application prepares a request. It may send the latest message together with conversation context, instructions and other data it considers relevant. If the chatbot uses a third-party model provider, the application may send the request to that provider’s service for processing.
The system generates a draft reply. A model uses the supplied request and its learned language patterns to produce text. A chatbot application can also retrieve information before it asks a model to draft an answer.
The reply is displayed or routed. The application shows the answer to the visitor, or its workflow may send a question to a human. Whether the original request, generated answer or related information is stored is a separate question from how the answer was produced.

For example, imagine a customer asks whether a parcel can be returned. A simple chatbot might respond using only the question and its general training. A business-support chatbot might retrieve the store’s returns policy and include the relevant passage in the request it sends to the model. A system connected to customer records could have a different data flow again.

Before using a tool, sketch the path in plain language: what the visitor submits, what the chatbot adds, which services receive it, where a human might see it, and what the user is told. If a provider’s documentation does not make that path clear, ask its support team before using the chatbot with customer or company information.

How do chatbots know how to respond? Context, personalization, and models

Information can shape a reply in different ways, and those uses should not be treated as interchangeable. A message may be used as context for the current answer, stored so a later conversation can refer to it, retrieved from a company knowledge base, or handled under a provider’s rules for improving models.

Start by separating these ideas:

Current conversation context: The chatbot may include earlier messages when responding to a follow-up. If you have already explained that you are asking about a particular order, the system may use that context to interpret “Can I change it?” This does not, by itself, tell you whether the conversation is saved after the reply.
Saved memory or personalization: Some systems can preserve information for later chats or personalize responses based on previous interactions. That feature may have separate controls from visible chat history. Check whether the setting is enabled and what it is intended to save.
Retrieved business information: A support chatbot can search a business’s own content and provide relevant passages to a model when answering a visitor. This is a way to supply current, company-specific information for a reply; it is not the same process as training a general-purpose model on that company’s content.
Model training or improvement: Large language models learn language patterns from training data. Separately, a provider may have settings or terms about whether conversations can be used to improve its services or models. Do not infer that choice from the chatbot’s answer style or from whether the conversation appears in history.

If you are setting up a customer-support chatbot, make a list of the content it is meant to use. Include sources such as policy documents and help articles, and decide what should not be included, such as confidential internal notes or customer records. Then check whether the tool retrieves only relevant content for a question or whether other data is also sent.

For a business example, momo answers from a company’s own content, checks a drafted answer against retrieved sources, and includes citations when it is confident. When it is not confident, it says so and opens a ticket for the team. Those answer and handoff steps do not establish how long information is retained, who can review it or whether it is used for model improvement. Check those matters separately.

Where do chatbots get their data? Check data-use and retention controls

Before a chatbot handles information from customers or coworkers, check the current terms and settings for the exact product and account you plan to use. Look for what is stored, how long it is retained, who may process or review it, whether it can be used for improvement, and what deletion or sharing controls are available.

Work through these checks:

Confirm which product and account you are using. A consumer chatbot, a business account and an API service may have different terms or controls. Do not assume a setting you saw in one account applies to another product from the same provider.
Look for retention details. Find out whether conversations or files are saved, how deletion works, and whether there are exceptions for legal, security or service reasons. Visible chat history is not proof that all stored copies have been removed.
Check human access and service providers. Read the current privacy terms and account settings to understand whether staff or other providers may process information. If you cannot tell what access is allowed, ask the provider before submitting sensitive data.
Check model-improvement settings. Look for a specific control or term covering whether conversation content may be used to improve models. Confirm that it applies to the account and service you actually use. Turning off one feature may not turn off storage or other forms of processing.
Review connected apps. If the chatbot can access files, calendars or other services, check which connections are active and what information they make available. Remove connections that the chatbot no longer needs.
Check sharing controls. Find out whether conversations can be shared through a link or another mechanism. Before sharing a conversation, review it for personal details, customer information and confidential business content.

Keep a short record of what you checked, when you checked it, and which team or account the decision covers. Provider terms and settings can change, so assign someone to revisit the decision when the tool or account changes. For customer support, document a clear rule for the team: which information is approved for prompts, which is not, and who can answer questions about exceptions.

Avoid exposing sensitive information in prompts or uploads

The safest starting point is to leave sensitive information out unless your organization has approved the tool and the data flow for that use. A chatbot needs enough context to answer a question, but it rarely needs every personal detail or a complete copy of an internal record.

Before you submit a prompt or file:

Remove secrets. Do not paste passwords, access tokens or recovery codes. If a chatbot only needs help troubleshooting, describe the error without sharing credentials.
Minimize personal details. Avoid payment details, health data and personal identifiers unless there is an approved reason and the tool has been reviewed for that purpose. A customer question about a return may need the store policy, not a customer’s full payment information.
Use placeholders. Replace names, email addresses, order references or other identifiers with neutral labels such as “[customer]” or “[order reference]” when the exact value is not needed. Treat this as a way to reduce exposure, not a guarantee: surrounding details in a prompt or document can still identify someone.
Check attachments before uploading. Review the document for comments, embedded details, hidden sheets or information on other pages that is irrelevant to the question. If you only need help with one paragraph, provide that paragraph rather than a full customer file.
Give the minimum useful context. Instead of pasting a whole conversation, summarize the issue and include only the policy or details needed for the answer. Keep confidential pricing, internal plans and customer histories out unless the use is approved.
Treat temporary modes cautiously. A temporary-chat option may affect history or personalization, but it is not the same as anonymity or a promise that nothing is retained. The provider still needs to process the message to generate a response.

For support teams, set these rules before asking staff to use a chatbot. Be specific about common situations: order questions, refund requests, product issues, customer complaints and internal escalation notes. A practical policy tells agents how to remove identifying details and what to do when a case cannot be summarized safely.

Delete chats carefully: history, backups, logs, and training differ

Removing a conversation from the visible chat list, deleting stored data and preventing future use for model improvement are different actions. Check the provider’s controls for each one rather than treating a single “delete” or “history” setting as proof that every copy has disappeared.

When you want to clean up a conversation, check:

Visible history: Does the chat disappear from the account’s history, and does this affect saved memories? Some products manage those features separately.
Backend deletion: What does the provider say happens after you request deletion? Check the stated timeline and whether copies can remain in logs or backups for a period.
Exceptions: Look for retention related to legal obligations, security, service operations or account administration. Ask the provider what applies to your account if the policy is unclear.
Model improvement: Find out whether the content was eligible for use in model improvement before you changed a setting or removed the chat. Do not assume that deleting a conversation reverses any earlier use in a training process; ask the provider what options and limitations apply.

If you manage a company account, have the account owner or privacy contact handle deletion requests that involve customer information. Review the Privacy Policy alongside the product-specific terms and settings; a general policy may not explain every control available for your account. Follow your organization’s recordkeeping rules and preserve anything that must be retained for a legitimate business reason. Deleting information just to make a visible history look tidy is not a substitute for understanding the organization’s retention obligations.

A deletion request and a training opt-out also solve different problems. If your goal is to stop future conversations from being used for a particular purpose, find the relevant setting and confirm its scope. If your goal is to remove past information, follow the provider’s deletion process and read what it covers.

If sensitive information went in, limit exposure and report it

If sensitive information has already been submitted, act promptly without assuming that deleting the visible conversation removes every copy. Limit further access, use the provider’s available controls, and follow your organization’s security or privacy process.

Take these actions as appropriate:

Stop sharing the conversation. If it was shared through a link or another feature, review the sharing settings and disable access where possible. Check whether anyone else received the link.
Delete the conversation and clear saved memory where available. These steps can reduce what remains accessible through the account, but they do not prove that logs, backups or other retained copies have been erased.
Revoke unneeded connections. If the chatbot was connected to a file store, calendar or other app, review that access and remove connections that are no longer needed.
Rotate exposed credentials. If a password, access token or recovery code was included, change or revoke it using the service that issued it. Do not rely on removing the chat as a substitute for protecting the account.
Notify the right person. Tell your organization’s security, privacy or support contact what was shared, where it was entered and when. Share only the information needed to assess the incident.
Ask the provider about next steps. Request details about deletion, retention exceptions and any account controls that apply. Keep a record of the response for the person responsible for handling the issue.

For a customer-support team, prepare an internal reporting route in advance. Staff should know who to notify and what details to include, without copying the sensitive information into another unapproved tool. If the disclosure involves a customer, follow your organization’s incident process rather than making promises about deletion or exposure that you cannot verify.

Troubleshooting: common chatbot privacy assumptions that fail

Several common assumptions confuse one kind of control with another. A temporary mode, a training opt-out and chat-history deletion can affect different parts of a service, so check each control’s scope and do not treat any one of them as a complete privacy setting.

“Temporary chat means anonymous.” A temporary mode may change whether a chat appears in history or informs later personalization. The provider still receives the message to generate a reply, and account or device information may also be involved. Read what the mode actually controls before using it for sensitive content.

“I turned off history, so the provider cannot retain anything.” Visible history and backend retention are separate matters. Check the provider’s explanation of deletion, logs, backups and exceptions rather than relying on what the chat list shows.

“Training is off, so the conversation is not stored.” A training or model-improvement opt-out may govern that use while leaving other storage or processing in place. Confirm the precise effect of the setting for the account you are using.

“A confident answer must be right.” A chatbot can sound certain and still be wrong. Verify consequential answers against a trusted policy, system of record or qualified person. In customer support, check refund promises, delivery statements and product claims before sending them to a customer.

“The chatbot can only see what I typed.” A system may receive prior messages or information from files and connected services, depending on its design and permissions. Review the conversation context and connected apps before using the tool.

“Deleting my account proves all copies are gone.” Account deletion is not, by itself, evidence that every stored copy has been removed. Check the provider’s deletion process and retention exceptions, and ask for clarification if you need to understand what applies.

Frequently asked questions

Can a chatbot see files or apps connected to my account, and where do chatbots get their data?

It depends on the chatbot and the permissions you granted. Some systems can use uploaded files or information from connected apps; others cannot. Review the tool’s access settings, check which apps are connected, and remove access that is no longer needed. Do not upload a file just because it is available: first check whether the chatbot needs it and whether it contains information you should not share.

Does turning off chat history also stop model training, and how do chatbots learn through conversation with users?

Not necessarily. Chat history, saved memory and model-improvement controls can be separate settings. Check the provider’s current terms and settings for the exact product and account you use. If the provider offers a training opt-out, confirm what it covers; do not assume that it also deletes stored conversations or changes other forms of processing.

Can a chatbot conversation be reviewed or shared with other people?

That depends on the provider, account, settings and sharing features. Review the current privacy terms to understand whether people may process the information and check whether conversations can be shared through links or other controls. Before sharing a transcript, remove personal, customer and confidential details that recipients do not need.

Is a temporary chat private or anonymous?

A temporary-chat mode may affect whether a conversation appears in history or is used for personalization, but that does not make the conversation anonymous or establish that no data is retained. The service still needs to process the message to generate a reply. Check the provider’s explanation of the mode and avoid entering sensitive information unless the use has been approved.

What should I do after pasting sensitive information into a chatbot?

Stop sharing the conversation, delete it and clear saved memory where the service allows, then review any public links or connected-app access. If a credential was exposed, rotate or revoke it. Notify your organization’s security or privacy contact and ask the provider about deletion and retention. Do not treat a deleted chat or closed account as proof that all copies are gone.

Make the workflow clear before use

Write down what information the chatbot receives, what it uses to answer, and what happens to the conversation afterward. Check the settings and terms for the exact account, keep sensitive details out unless approved, and give your team a clear route for handling mistakes.

For a practical look at using a chatbot in customer support, read the guide for support teams. If you want to see a business-content-based support workflow, try momo free.