What is an AI Hallucination in Plain English?

Generative AI tools like ChatGPT and Google Gemini have transformed how we conduct preliminary research, digest lengthy reports, and summarize complex data—especially within family office environments. But as impressive as these models appear, they harbor a hidden risk: AI hallucinations. Simply put, this is when an AI confidently makes something up that isn’t true.

In this post, I aim to clarify the AI hallucination definition for non-technical readers and outline why these errors are particularly salient for family offices working with limited or private markets data. We’ll explore the tension between producing “faithful” but possibly erroneous responses and completely fabricated information, and the governance mindset necessary to safely integrate generative AI into your office’s workflows.

What Is an AI Hallucination? The Plain English Definition

AI hallucination refers to situations where a generative AI model generates an output that is plausible sounding but factually incorrect or entirely fabricated. In other words, the AI confidently makes something up without basis in the data it was trained on or the information it was given.

Think of it like a highly articulate storyteller who fills in gaps with creative but false details. The AI doesn’t "know" it’s incorrect — it simply predicts text based on patterns without true understanding or factual verification.

Examples of AI Hallucinations

• ChatGPT providing a fake citation to a research paper that doesn’t exist.
• Google Gemini creating a plausible but invented company financial metric when asked to parse a private placement memorandum.
• Summaries that omit crucial disclaimers or timelines, leading to misinterpretation.

Despite seeming authoritative, these generative AI errors are misleading and potentially damaging if taken at face value—especially when making investment or operational decisions.

Why Do AI Hallucinations Happen?

Generative AI models are trained on massive datasets to predict the next word or token given a prompt. Their outputs are based on statistical likelihoods and pattern recognition, not fact-checking or comprehension. Several factors contribute to hallucinations:

1. Training Data Limitations: The AI’s knowledge is inexact and uneven, sometimes outdated, and often lacks domain-specific granularity.
2. Ambiguous Inputs: Vague or complex prompts invite the AI to “guess” what is being asked.
3. Private / Scarce Data: In family offices, private markets data scarcity means fewer examples in public data. The AI is more likely to fabricate plausible-sounding but invented details.

For example, ChatGPT and Google Gemini may not have access Helpful hints to proprietary private equity fund reports or internal due diligence. When asked about these, their responses must be treated with caution.

Why AI Hallucinations Are Persuasive and Risky

A key challenge is the persuasive nature of AI hallucinations. These tools produce fluent, well-structured text that can sound very authoritative. The AI doesn’t hedge with “I’m not sure,” but instead states information confidently. This often leads users to trust inaccurate outputs unknowingly.

In a family office setting, this can be problematic because:

• Decision-Making Impact: Erroneous preliminary research or flawed summaries can mislead investment committees.
• Compliance Risks: Misstated facts or missing disclosures might not hold up under auditor scrutiny.
• Operational Inefficiencies: Time wasted verifying or correcting AI-generated misinformation.

The Auditor’s Lens: “What Would We Show Them?”

When adopting any generative AI tool, a crucial mindset I recommend is to ask:

“What would we show an auditor as evidence of this output’s validity?”

If the AI’s answer can’t be reconciled back to verifiable source data or known documentation, it should not be trusted as a factual source.

Family Office Use Cases: When Hallucination Risks Are Elevated

Family offices typically use tools like ChatGPT or Google Gemini for tasks such as:

• Preliminary research on market sectors or specific companies.
• Parsing complex investment reports and documents.
• Summarizing meeting notes, due diligence call transcripts, or fund manager updates.

Because of the private markets data scarcity—many family office deals involve non-public companies or bespoke structures—AI may lack a foundation to generate fully accurate information on these topics. The risk of hallucinating numbers, dates, or statuses is higher than for public market reference data.

Example: Parsing a Private Placement Memo

Imagine feeding an AI a private placement memorandum (PPM) for a venture fund. The model might produce a neat summary—but inaccuracies might include:

• Incorrectly stating the fund size.
• Misattributing investment strategies or timelines.
• Ignoring risk factors or disclaimers embedded in fine print.

Because family offices rely heavily on trusted interpretations of these documents for manager due diligence and reporting, any hallucination could propagate flawed assumptions internally.

Faithfulness vs. Factual Hallucinations: The Nuance

Not all hallucinations are equal. There is an important difference between:

Faithfulness Factual Hallucinations Outputs that stick closely to source data but may omit exceptions or nuances, causing partial inaccuracies. Complete fabrications without any basis in source data, such as made-up facts, dates, or names. Example: Summarizing a report but failing to mention a key caveat. Example: Inventing a financial metric that never appeared in any documentation.

Generative AI often balances on this continuum. Family offices need to discern not only if the summary or answer is factually correct but also if it is faithful—meaning it accurately represents key aspects without glossing over important details.

Mitigating AI Hallucination Risks in Family Offices

To safely adopt ChatGPT, Google Gemini, or similar tools, consider these governance principles:

1. Human-in-the-Loop Verification: Always verify AI outputs against trusted documents or experts before acting on them.
2. Limited Scope: Use generative AI only for preliminary research or first drafts, never final decisions.
3. Data Governance: Maintain control over sensitive data and restrict AI access to internal private data sets.
4. Auditable Trails: Log prompts and AI outputs in a way that allows retrospective review by compliance teams and auditors.
5. Training & Awareness: Educate all users about hallucination risks and red flags to watch for.

Conclusion

In summary, the AI hallucination definition—when a generative AI confidently makes something up—helps us understand a key vulnerability in tools like ChatGPT and Google Gemini. For family offices dealing with scarce private markets data and complex investment documentation, these generative AI errors pose real risks.

To harness the productivity benefits of AI while managing the dangers, family offices must adopt a rigorous governance mindset and never treat AI output as fact without verification. After all, the question we always ask is:

“What would we show an auditor?”

Keeping this question front and centre is the best way to ensure generative AI becomes a trusted assistant—not an unreliable oracle.

Author: Former buy-side investment researcher turned family office technology risk lead, with 12 years of experience across UHNW reporting, manager due diligence, and data governance in Canada.