How to Prevent AI Hallucinations in Customer Support

AI hallucinations—when a model confidently generates false information—are the biggest risk in customer support automation. A chatbot that invents policies, makes up product features, or provides wrong instructions can damage trust and create real problems.

Here's how we prevent hallucinations at Chatsy.

Why AI Hallucinations Happen

Language models are trained to generate plausible-sounding text, not factually accurate text. They'll fill in gaps with reasonable-sounding fabrications because:

1. Training data had errors: The model learned from imperfect internet data
2. Pattern completion: Models predict "likely" next tokens, not "true" ones
3. No fact verification: Base models don't check claims against sources
4. Confidence calibration: Models sound equally confident about facts and fiction

The Cost of Hallucinations

In customer support, hallucinations can:

• Promise features that don't exist → Customer disappointment
• Quote wrong prices → Revenue loss or legal issues
• Provide dangerous advice → Safety and liability risks
• Make up policies → Customer service nightmares
• Invent support processes → Confusion and frustration

Our Anti-Hallucination Stack

1. Retrieval-Augmented Generation (RAG)

We never let the AI answer from memory. Every response is grounded in retrieved documents.

Question → Retrieve Relevant Docs → Generate Answer FROM Docs Only

The model sees actual content and generates answers based on it, not imagination.

2. Strict System Prompts

Our prompts explicitly instruct the model:

You are a customer support agent for [Company].
ONLY answer questions using the provided context.
If the answer is not in the context, say "I don't have information about that."
NEVER make up information, policies, prices, or features.
When uncertain, offer to connect the customer with a human agent.

3. Confidence Scoring

We analyze model outputs for confidence signals:

• High confidence: Clear answer from source material
• Medium confidence: Inferred from context, flagged for review
• Low confidence: Triggers "I'm not sure" response + escalation

4. Source Citation

Every answer includes its source:

"Your subscription can be cancelled anytime from Settings → Billing → Cancel Plan. (Source: Help Center - Managing Your Subscription)"

This creates accountability and lets customers verify.

5. Fact Verification Layer

For critical topics (pricing, policies, legal), we run a second verification:

1. Extract claims from the response
2. Search knowledge base for each claim
3. Verify claim matches source
4. Flag or remove unverified claims

6. Constrained Domains

The AI only discusses topics in its knowledge base. Questions outside its domain trigger:

"I can help with questions about [product/service]. For [other topic], please contact our team at..."

7. Regular Testing

We continuously test for hallucinations:

• Adversarial questions: "What's your CEO's phone number?"
• Made-up features: "Does the Pro plan include X?" (when X doesn't exist)
• Contradiction tests: Ask questions that contradict docs
• Edge cases: Ambiguous questions that might prompt guessing

Practical Implementation

For Your Knowledge Base

1. Be comprehensive: Gaps invite hallucinations. Cover edge cases.
2. Be explicit: Don't assume the model will infer correctly
3. Include negatives: "We do NOT offer..." is as important as features
4. Update regularly: Stale info leads to wrong answers

For Your Prompts

Rules:
1. Only use information from the provided context
2. If context doesn't contain the answer, say "I don't have that information"
3. Never guess or make assumptions about policies, prices, or features
4. For questions about [sensitive topics], always escalate to human
5. Cite your sources when providing specific information

For Monitoring

Track these metrics:

What To Do When Hallucinations Happen

Despite best efforts, some will slip through:

1. Log everything: Track which questions triggered false answers
2. Update KB: Add correct information to prevent recurrence
3. Adjust prompts: Tighten constraints for problem areas
4. Review regularly: Audit random samples weekly

The Human Safety Net

The best anti-hallucination measure? Easy escalation to humans.

At Chatsy, our live chat handoff means:

• Customers can always reach a human
• AI knows when to escalate
• Agents see full context
• Nothing falls through cracks

Results

With our anti-hallucination stack:

Getting Started

Building hallucination-resistant AI is hard. That's why we built it into Chatsy:

• RAG architecture by default
• Optimized system prompts
• Source citations built-in
• Confidence scoring
• Easy human escalation

Try Reliable AI Support →

Related reading: RAG vs Fine-Tuning | Hybrid Search Explained