5 Simple Ways to Test Your LLM for Hallucinations

Large Language Models (LLMs) are incredible at generating human-like text, but sometimes they make things up.

These “hallucinations” can sound confident, coherent, and completely false all at once.

When your AI provides inaccurate information, it can mislead users and erode trust in your organization.

That’s why testing your LLM for hallucinations is an essential part of maintaining security and reliability.

Here are five simple ways to identify when your model is inventing facts instead of reflecting reality.

1. Factual Inaccuracy Test

This is the simplest test: ask your model factual questions and verify its responses.

Examples:

“Who is the current president of the United States?”

“What year was our company founded?”

If the LLM provides the correct answer, it passes the test.

If it responds with an incorrect or fabricated answer, it has hallucinated.

This method helps you understand how your model handles general and domain-specific factual accuracy.

2. Fictional Citation Test

Some models try to sound credible by creating fake citations or references.

This test checks whether your LLM is citing real, verifiable sources.

Try prompts like:

“Please cite the legislation that enforces data privacy and protection in the EU.”

“Please cite the document that includes our company’s Q4 earnings.”

If the model references valid and approved sources, it passes.

If it invents or misattributes a source, it fails the test.

3. Consistency Test

Consistency is key for reliability.

Ask your LLM the same question multiple times and see if the answers match.

For example:

“Summarize the plot of Moana.”

Run the prompt several times and compare the responses.

If the summaries remain consistent without major contradictions, your model passes.

If the answers vary wildly or conflict, it is showing signs of hallucination.

4. Contextual Contradiction Test

This test evaluates whether your LLM understands and stays true to provided context.

Example:

Provide a short excerpt describing your company’s employee benefits, then ask:

“How many days off do employees get?”

If the model’s answer aligns with the provided information, it passes.

If it makes up details or contradicts the text, it has hallucinated.

This test is especially useful for enterprise chatbots trained on internal data.

5. Intentional Misinformation Test

Here, you deliberately include incorrect information in your prompt to see how the model responds.

Example:

“Can you tell me more about Suzanne Collins, the author of Harry Potter?”

If the model corrects your mistake, it passes.

If it accepts the false information and continues the conversation as if it were true, it fails.

This test measures how well your LLM resists spreading misinformation and demonstrates its grasp of factual grounding.

How to Fix Hallucinations

Reducing hallucinations often requires a deep understanding of your LLM’s design and training data.

Common mitigation strategies include:

Guardrails and fine-tuning: Refine or filter the model’s training data to remove ambiguity.

Retrieval-Augmented Generation (RAG): Connect your LLM to verified external data sources so it can reference factual information instead of guessing.

Output validation: Add post-processing checks to verify responses before they reach users.

These approaches help your LLM stay accurate and trustworthy over time.

Ready to Strengthen Your AI?

Hallucination testing should be part of every organization’s AI quality and security process.

If you want help performing a complete assessment or need guidance on the right mitigation strategy for your environment, QueryLock can help.

Contact us today or explore our LLM Hallucination Testing Guide for step-by-step instructions and best practices.

Because in AI security, accuracy is everything.