How to Reduce AI Hallucinations and Bias Through Prompting

Research from University of Warwick shows asking AI "Could you be wrong?" immediately makes the model reveals hidden knowledge about limitations, contradictory information, and biased reasoning. This simple 4-word follow-up question reduces both AI hallucination and bias by activating self-critique mechanisms that remain dormant in standard interactions.

The Problem: Confident but Wrong

AI hallucination and bias - when language models generate confident but incorrect or skewed responses - are widespread problems across major platforms like ChatGPT and Claude. A breakthrough University of Warwick study discovered a simple 4-word question that reduces AI misinformation and biased reasoning by activating hidden self-critique mechanisms: "Could you be wrong?"

You might be wondering why this works when AI systems seem so confident in their responses. Here's what most people get wrong about AI accuracy - the system contains vast amounts of contradictory information and uncertainty markers, but system prompts optimize for confident responses rather than balanced analysis. This same mechanism that hides uncertainty also conceals awareness of potential biases in reasoning.

What Are AI Hallucinations and Why Do They Happen

AI hallucinations occurs when language models generate confident, detailed responses that are factually incorrect or completely fabricated. The AI doesn't "know" it's making things up - it generates responses based on statistical patterns that seem plausible but have no basis in reality.

Common examples of AI hallucinations include:

• Creating fake research studies with realistic-sounding titles and authors
• Inventing historical events that never happened
• Generating non-existent product features or technical specifications
• Fabricating quotes from real people
• Making up legal precedents or medical conditions

Hallucination happens because AI systems are trained to generate coherent, helpful responses even when they lack specific knowledge. When faced with unfamiliar topics, the system fills gaps by combining patterns from its training data in ways that sound authoritative but may be completely wrong.

What is AI Bias and Why It Happens

AI bias happens when systems provide skewed, incomplete, or prejudiced information while appearing objective and comprehensive. Unlike hallucination, biased information may contain factual elements but presents them in ways that favor certain perspectives, groups, or conclusions.

AI bias manifests in several forms:

Selection bias: Choosing which facts to include or emphasize while omitting contradictory evidence

• Example: Discussing benefits of a controversial policy while ignoring documented drawbacks

Representation bias: Overrepresenting certain groups, viewpoints, or cultural perspectives

• Example: Defaulting to Western perspectives when discussing global issues

Confirmation bias: Presenting information that confirms popular beliefs or user expectations

• Example: Supporting widely-held misconceptions because they appear frequently in training data

Historical bias: Reflecting past prejudices present in training materials

• Example: Career suggestions that reflect historical gender stereotypes

Recency bias: Overweighting recent information or trends while ignoring established patterns

• Example: Treating temporary market movements as permanent shifts

Authority bias: Giving disproportionate weight to information from perceived authorities

• Example: Prioritizing statements from prestigious institutions even when contradicted by broader evidence

AI bias happens for three main reasons:

1. Training data bias: AI systems learn from vast datasets that reflect existing societal biases, historical prejudices, and unequal representation. If women are underrepresented in engineering articles, the AI may perpetuate stereotypes about technical careers.
2. Algorithmic optimization: AI systems are optimized to generate responses that users find satisfying and helpful, which can reinforce popular opinions over accurate or balanced information.
3. Statistical amplification: When certain viewpoints appear more frequently in training data - whether due to historical dominance, louder voices, or systematic exclusion of minorities - AI systems treat frequency as a signal of truth or importance.

Why Both Problems Occur Together

Both phenomena appear across all major AI platforms, with particular challenges in:

• Medical and health information
• Recent events and current statistics
• Specialized technical topics
• Legal precedents and interpretations
• Social, cultural, and political topics
• Historical events and scientific research

The root cause isn't lack of knowledge - it's how AI systems are trained to prioritize user satisfaction over accuracy and balanced representation. Key factors that enable both hallucination and bias include:

1. System prompts that instruct AI to provide helpful, confident responses that satisfy immediate requests
2. Statistical optimization that generates outputs likely to match user expectations rather than factual accuracy
3. Training data bias that reflects existing prejudices, incomplete information, or popular misconceptions present in source materials

The University of Warwick Study Results

Dr. Thomas Hills, a psychologist at the University of Warwick, designed experiments to test whether AI systems possess hidden knowledge about their own limitations and biased reasoning. The results were striking and have major implications for how we interact with AI.

The Fictional Disease Experiment: Exposing Hallucination

Hills asked various AI models medical questions about "Glianorex Hyperactivity Disorder" - a completely fictional condition invented for the study.

Initial AI Response: Confident analysis of patient profiles, genetic factors, and treatment recommendations for the non-existent disease.

After "Could you be wrong?": Immediate recognition that the disorder was fictional, explanation of reasoning based on analogies to real conditions, and acknowledgment of the fundamental error.

The Choice Overload Bias Test: Revealing Hidden Contradictory Evidence

When asked about "choice overload" - the popular idea that having too many options paralyzes decision-making - AI models enthusiastically described the phenomenon and cited supporting studies, presenting a one-sided view of the research.

What they omitted: A major meta-analysis of 50 studies found the effect size to be "virtually zero," contradicting the popular narrative.

After "Could you be wrong?": AI immediately surfaced all contradictory research it had previously hidden, providing a nuanced view of the actual scientific evidence and acknowledging the bias toward presenting popular psychological concepts as established fact.

The Key Discovery: Hidden Self-Awareness

The surprising thing researchers discovered was that AI systems consistently possess knowledge that contradicts their initial responses and awareness of their own potential biases, but this information remains locked away until specifically prompted. This reveals that both hallucination and bias often stem from selective information presentation rather than lack of knowledge.

Step-by-Step Guide: The 4-Word Method for Reducing Hallucination and Bias

1. Make "Could you be wrong?" Your Default Follow-Up

For any important AI response, automatically ask "Could you be wrong?" This simple habit activates critical thinking processes that remain dormant in standard interactions, helping surface both factual errors and biased reasoning.

How to implement:

• Create a mental habit: Never accept important AI outputs without the follow-up question
• Add "Could you be wrong?" to your AI prompt templates
• Train team members to use this phrase in collaborative AI sessions

2. Implement Two-Stage Workflows

Rather than treating AI responses as final answers, treat them as initial hypotheses. Use the follow-up critique to identify gaps, verify accuracy, and surface alternative perspectives.

The structured approach:

• Stage 1: Ask your initial question and receive the AI's response
• Stage 2: Always ask "Could you be wrong?" and carefully analyze the self-critique
• Stage 3: Use insights from the critique to refine your original question or ask for additional information
• Stage 4: Repeat the cycle until you have comprehensive coverage of the topic

Professional workflow example:

Initial prompt: "Write a marketing strategy for launching our new fitness app to millennials."

AI response: [Provides confident strategy focusing on social media, influencer partnerships, etc.]

Follow-up: "Could you be wrong?"

AI self-critique: "Yes, I could be wrong. I made assumptions about millennials' fitness preferences without considering geographic variations, income levels, or the fact that millennials are now aging into different life stages. I also didn't account for your specific app features, competitive landscape, or budget constraints."

Refined approach: Use the critique to ask more specific questions about target sub-segments, competitive analysis, and budget-appropriate tactics.

3. Use Advanced Iteration Techniques

For sophisticated projects, ask multiple rounds of follow-up questions. Each iteration reveals different layers of potential error and bias that compound into significantly better final outputs.

The iterative deep-dive method:

• Round 1: "Could you be wrong?" - Often reveals obvious errors, missing context, or one-sided presentations
• Round 2: "What else might you be wrong about? Consider methodological issues and potential biases" - Uncovers reasoning flaws, systematic biases, and alternative perspectives
• Round 3: "Are there alternative viewpoints or contradictory evidence I should consider?" - Reveals opposing arguments, minority opinions, and suppressed research
• Round 4: "What would someone who disagrees with this analysis say?" - Generates counter-arguments and identifies potential blind spots in reasoning

4. Develop Advanced Questioning Techniques

Beyond the basic 4-word method, these questions activate similar self-critique mechanisms to combat both hallucination and bias:

For fact-checking and accuracy:

• "What evidence would contradict this conclusion?"
• "What assumptions are you making that might not be true?"
• "What additional information would be needed to be more certain?"

For identifying bias and missing perspectives:

• "Who would disagree with this analysis and why?"
• "What viewpoints or voices are missing from this response?"
• "How might someone from a different background see this issue?"
• "What would change your confidence level in this answer?"

Why This Matters More Than Ever

As AI systems become more sophisticated and integrated into critical decision-making processes, both hallucination and bias problems are becoming increasingly dangerous. Consider the implications:

• Business decisions based on confidently presented but flawed or biased market analysis
• Medical consultations where AI provides detailed but incorrect diagnostic reasoning or overlooks health disparities
• Educational content that perpetuates popular myths while ignoring contradictory evidence or marginalized perspectives
• Legal research that misses crucial precedents, misinterprets statutes, or reflects historical biases in legal reasoning
• Hiring and evaluation where AI recommendations reflect training data biases around gender, race, or background

The more convincing AI becomes, the more critical it becomes to have reliable methods for accessing hidden uncertainties, contradictory knowledge, and awareness of biased reasoning patterns.

The Meta-Skill: Building Skeptical Thinking

The ultimate goal is developing intuition for when AI might be overconfident. This comes from practice and pattern recognition. The more you use these techniques, the better you'll become at identifying subtle signs that warrant deeper questioning, even before you ask "Could you be wrong?"

Building your skeptical mindset:

1. Assume AI confidence doesn't equal AI accuracy or objectivity
2. Look for what's missing from responses, not just what's included
3. Consider who benefits from the AI's recommendations and whose voices might be absent
4. Ask yourself: "What would I need to verify this independently?"
5. Question whether the response reflects popular opinion rather than complete evidence

The Bottom Line

The next time you're working with AI on something important, remember that the system's first response is just the beginning of the conversation.

The most effective AI interactions may not be single-prompt conversations but iterative dialogues where we systematically challenge the system to examine its own reasoning. This approach transforms AI from an overconfident assistant into a self-reflective thinking partner that acknowledges both its factual limitations and potential biases. You might be surprised by what the AI knows about contradictory evidence and alternative perspectives but isn't telling you.

Start today: Make "Could you be wrong?" your default follow-up question for any important AI interaction. This simple habit could save you from costly mistakes and help you access more balanced, complete information from these systems.

Matthew T. Grote ran an AI implementation lab for the government that saved agencies millions of dollars. His work focused on identifying strategic outcomes for AI implementations, developing AI solutions, and creating practical frameworks for human-AI collaboration in high-stakes environments.

Business Inquiries: mgrote@generatethefuture.ai

Source: Hills, T.T. (2025). "Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making." University of Warwick