What Happens When Training Data Contradicts Itself
How Contradictions Get Into Training Data
Most contradictions are not intentional. They accumulate over time through normal business operations:
- Multiple versions of the same document. You updated your pricing in January, but the old pricing PDF is still in the training data alongside the new one.
- Different departments wrote different things. Marketing says "free 30-day trial" while the terms of service say "14-day trial period." Both documents are in the knowledge base.
- Information changed but old content was not removed. Your return policy used to be 60 days, now it is 30 days, and both versions exist in your training data.
- Website crawl captured outdated pages. You crawled your website for training data, but some pages had not been updated to reflect recent changes.
- FAQ answers conflict with detailed documentation. A quick FAQ answer simplifies something in a way that technically contradicts the detailed explanation elsewhere.
What the AI Does With Contradictions
When vector search retrieves chunks that contain contradictory information, the AI model has to decide what to do. Different models handle this differently, but common behaviors include:
Blending both answers
The AI combines information from both chunks into one response. For example, if one chunk says "Standard shipping takes 3 to 5 business days" and another says "Delivery is typically 5 to 7 business days," the AI might say "Shipping takes 3 to 7 business days," which sounds reasonable but is not what either document actually says.
Picking one at random
Depending on which chunks score higher in the vector search and how the AI weights them, it might cite one version in one conversation and the other version in a different conversation. This creates an inconsistent experience where two customers get different answers to the same question.
Hedging
More capable models may notice the contradiction and qualify their answer: "According to our documentation, shipping may take 3 to 5 or 5 to 7 business days depending on..." This is the best outcome of a bad situation, but the user still does not get a clear answer.
How to Find Contradictions
Start with content that, if wrong, would cause real problems: pricing, policies, legal terms, product specifications, and contact information. Search your training data for all mentions of each critical fact and verify they match.
Ask your chatbot the same factual question five times in separate conversations. If you get different answers, there is likely a contradiction in the training data. See How to Test If Your AI Learned the Right Information.
Go through your list of uploaded documents and check each one against the current version of that information. Anything that has been updated since it was uploaded is a potential contradiction source.
How to Fix Contradictions
Delete the outdated version
The most direct fix. Remove the old chunks from your knowledge base and keep only the current, accurate version. See How to Delete or Update Specific Training Data.
Create a single authoritative source
Instead of having pricing mentioned across five different documents, create one authoritative "Pricing and Plans" document that becomes the definitive source. Remove pricing information from other training documents to eliminate the possibility of drift.
Add dates to time-sensitive content
If you need to keep historical information (for compliance or reference purposes), add clear date markers: "As of March 2026, our standard shipping time is 3 to 5 business days." This gives the AI context to determine which version is current.
Set up a review schedule
Contradictions happen over time. Set a monthly or quarterly review to check your training data against your current business information. See How to Keep Your AI Training Data Up to Date.
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