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How to Delete or Update Specific Training Data

You can delete or replace specific training data chunks from your AI knowledge base at any time through the admin panel. When information changes, removing the old data and uploading the new version ensures your chatbot always gives current, accurate answers instead of citing outdated content.

Why You Need to Update Training Data

Training data is not something you set and forget. Business information changes constantly: pricing adjusts, policies update, products launch, staff changes, procedures evolve. Every change in your business that affects what your chatbot says requires a corresponding update to the training data.

If you do not update, two problems emerge. First, the chatbot gives outdated answers that may frustrate or mislead customers. Second, if both old and new information exist in the knowledge base simultaneously, the AI may blend contradictory information into confusingly wrong responses.

How to Delete Training Data

Step 1: Go to your chatbot's knowledge base.
In the admin panel, open the AI Chatbot app and select the chatbot whose training data you want to modify. Navigate to the knowledge base or embeddings section.
Step 2: Find the chunks to remove.
Your training data is stored as individual chunks. Each chunk has a tag (the label you gave it when uploading) and a preview of its content. Browse or search for the chunks you need to remove.
Step 3: Delete the selected chunks.
Select the chunks and delete them. This removes both the text content and the vector embeddings from the database. The deletion is immediate, and the chatbot will no longer retrieve that information when answering questions.

How to Update Training Data

There is no "edit in place" function for training data chunks because changing the text requires regenerating the vector embedding. The update process is delete and re-upload:

Step 1: Delete the outdated chunks.
Follow the deletion steps above to remove the old content.
Step 2: Prepare the updated content.
Write or gather the new version of the information. Make sure it is current, accurate, and properly organized. See How to Organize Training Data for Best Results.
Step 3: Upload the new content.
Upload the updated text as new training data. The platform will chunk it and generate new embeddings. Use the same tag as before if you want to keep your training data organized by the same labels. The cost is 3 credits per new chunk.

When to Update vs When to Add

Not every change requires deleting old data:

Bulk Updates

If you need to update a large portion of your training data (a full website recrawl, a new version of your documentation), it is often easier to delete all chunks with a specific tag and re-upload everything fresh. Tags make this manageable because you can delete by tag rather than hunting for individual chunks.

Tagging strategy: When you first upload training data, use descriptive tags like "pricing-2026" or "return-policy" or "product-catalog." When it is time to update, you can quickly find and replace all chunks with a specific tag. Vague tags like "docs" or "upload1" make maintenance harder later.

Verifying Your Updates

After updating training data, test the chatbot with questions that should now produce different answers. Ask the specific questions that prompted the update in the first place. If the chatbot still gives the old answer, check that the old chunks were actually deleted and the new ones were uploaded successfully. See How to Test If Your AI Learned the Right Information.

Keep your AI chatbot current with easy training data management. Update anytime, no coding needed.

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