How Do I Tell if ChatGPT is Hallucinating?

Artificial intelligence (AI) tools like ChatGPT and Google's Gemini are revolutionizing how professionals, including family office teams, access and process information. From preliminary research to parsing complex reports and generating summaries, these AI assistants streamline workflows and offer impressive convenience. However, beneath their polished prose and confident answers lurks a significant risk: AI hallucinations.

In this post, I’ll explain what AI hallucinations are, why they matter especially in the world of ultra-high-net-worth (UHNW) family offices, and practical steps you can take to identify and mitigate them. Given the scarcity of data in private markets and the nuanced, sometimes subjective nature of wealth management, understanding when AI may be “making stuff up” isn’t just academic — it’s essential for preserving trust and accuracy.

What Are AI Hallucinations?

Simply put, an AI hallucination occurs when a generative AI model produces information that is factually incorrect, fabricated, or misleading, despite sounding plausible and confident. This phenomenon happens because models like ChatGPT or Google Gemini generate text by predicting sequences of words based on training data rather than consulting a verified database of facts.

There are two main types of hallucinations worth distinguishing:

• Factual hallucinations: The AI states things that are objectively false — incorrect dates, false quotes, nonexistent studies, or fabricated statistics.
• Faithfulness or attribution issues: Here, the AI may paraphrase or blend information inaccurately, misattribute statements to wrong sources, or distort data context, leading to subtly misleading outputs.

Why Are Hallucinations Especially Risky in Family Office Use Cases?

Family offices operate at the intersection of especially sensitive, complex, and scarce information domains. The stakes are high. Key use cases involving AI include:

• Preliminary research: Getting a quick overview of investment themes, market trends or regulatory updates.
• Parsing dense reports: Summarizing lengthy fund prospectuses, private equity documents or due diligence files.
• Generating summaries and decision support: Transforming raw data into actionable bullet points or memos for advisors and family principals.

The problem lies in the scarcity and opacity of private markets data. Public equities boast abundant, verified datasets accessible by APIs and trusted sources. Private markets, less regulated and less transparent, provide limited publicly available information. When an AI model tries to synthesize or generate data in such domains, it has a weaker factual basis and a higher tendency to hallucinate.

How to Detect When ChatGPT or Google Gemini Might Be Hallucinating

Hallucinations in AI outputs tend to be persuasive — the fluency and coherence of language create an illusion of accuracy. But a few practical strategies can help you detect when your AI assistant may be off-path.

1. Always Perform AI Fact Checking With Independent Sources

– Cross-reference important statements against reputable sources, ideally primary ones. For public market data, government filings, official databases, or credible news outlets work well.

– For private markets, where fewer public benchmarks exist, check with trusted internal data, manager reports, or confirm facts via direct communication with counterparties.

– Use standard financial research tools and databases your family office relies on, such as Bloomberg, PitchBook, or Preqin, to verify AI-supplied facts.

2. Ask for Source Citations and Evaluate Them Critically

– When ChatGPT or Gemini provide “sources,” don’t take them at face value. AI models often generate plausible-looking citations that don’t actually exist or are only loosely related.

– Evaluate whether the sources named are authoritative and accessible.

– Press the AI to show direct quotes and verify those quotes are accurate and in context.

3. Test for Consistency by Rephrasing or Re-asking

– Ask the same question in different ways or ask for detailed explanations about specific data points.

– Hallucinations tend to shift or get contradicted in follow-up queries, while well-grounded facts remain consistent.

4. Use Domain Expertise and Intuition As a Filter

– Often, the first sign of hallucination is a subtle “off” feeling from an informed user. For example, a claimed return metric for a private equity fund that exceeds known market norms by a wide margin should raise red flags.

– Train your team to flag AI-generated outputs that sound too good to be true or appear out of place.

5. Leverage AI Governance and Validation Tools

– Emerging tools are trying to build fact-checking into workflows. Some offer integration that cross-checks generative private markets research AI output with verified data—though these remain nascent.

– Maintain clear audit trails of AI interactions, store output logs, and document corrections.

Faithfulness vs. Factual Hallucinations: What’s the Difference?

This distinction matters in practice:

Faithfulness Issues Factual Hallucinations Information is partly true but improperly attributed or reinterpreted. The information is completely fabricated or false. Errors stem from misrepresentation or blending of correct data. Errors stem from invention without basis. Example: Misquoting a fund manager or mixing up dates. Example: Inventing a nonexistent fund or performance metric.

Both types of errors are problematic but often require different mitigation strategies. Faithfulness errors may be caught by reviewing source documents carefully, while factual hallucinations require more fundamental validation and skepticism.

Approaching ChatGPT Wrong Answers with Healthy Skepticism

The AI research community and users alike have learned that models like ChatGPT can sound very confident while confidently delivering wrong answers — what I keep track of in my personal note file as “things AI got wrong.” When your family office team uses ChatGPT, consider these best practices:

1. Embed human oversight: Treat AI output as a starting point, not a final deliverable.
2. Ask “What would we show an auditor?” before trusting or sharing AI-generated content — is there a verifiable source or trackable audit trail?
3. Document any corrections or clarifications made during AI-assisted research or report generation.
4. Foster a culture where AI skepticism is encouraged, especially for details involving private markets or high-impact decisions.

Comparing ChatGPT and Google Gemini

Both ChatGPT and Google Gemini utilize advanced Large Language Models (LLMs) trained on massive datasets, yet their hallucination risks remain similar. Gemini aims to advance context understanding and multi-modal inputs, but as of now, the fundamental challenge of hallucinations persists across platforms.

Until reliable, real-time fact-checking is embedded deeply into these AI workflows, users must independently verify outputs. No model is immune.

Summary: How to Verify AI Sources and Protect Your Family Office Data Integrity

• Be vigilant: Understand AI tools can hallucinate with confidence.
• Fact-check rigorously: Cross-verify facts with trusted internal or external data sources.
• Request citations: And critically assess their validity and relevance.
• Test the AI: Check for internal consistency and re-query suspicious claims.
• Rely on expertise: Use domain knowledge to flag unlikely outputs.
• Keep audit trails: Log AI interactions for governance and compliance.
• Be skeptical: Always ask “What would an auditor want to see?” to ensure accountability.

Generative AI tools like ChatGPT and Google Gemini can be transformational helpers, but as family office technologists and researchers, our responsibility is to ensure these tools augment human insight—not replace careful due ChatGPT accuracy diligence.

By proactively recognizing and managing AI hallucinations, your team can harness the power of these tools while safeguarding the integrity of your private markets insights and family wealth stewardship.