The Genealogist’s Secret Weapon: Why "Domain Knowledge" is the Antidote to AI Hallucinations

Not long ago, I found myself staring at a record review generated by a cutting-edge AI. It was fast, it was clean, and at first glance, it was impressive. But as I looked closer, I felt that familiar "genealogical itch"—the one that tells you something is fundamentally wrong even when the data looks right.

The AI had linked a family in 1700s Maryland to one set of records. To the AI, it was a logical match of the places, names and ages. To me, the dates and specific geographic movements were historically impossible.

In the tech world, they have a term for what saved me from that error: Domain Knowledge.

The Generalist vs. The Specialist

We often treat AI like a specialized researcher, but in reality, it is a "Generalist." It has "read" the entire internet, but it hasn't lived in the archives. It understands the probability of words, but it doesn't understand the "ground truth" of a specific time or place.

This is where the concept of the "Human-in-the-loop" becomes more than just a catchphrase. In our research, the AI is the engine, but we are the steering wheel. The AI can process a thousand deeds in the time it takes us to read one document, but it may not know that a "Third Great-Uncle" mentioned in a Southern will might actually be a cousin, or that a "natural" son carries a very specific legal weight.

The Integrity Filter

As we move toward RootsTech 2026, the term "hallucination" is becoming part of our daily vocabulary. We’ve all seen it—the AI confidently "invents" a parent or a birthdate to fill a gap in the logic.

This is where your years of experience become an Integrity Filter.

Your domain knowledge is the mental database of naming patterns, migration trails, and local laws that the AI simply cannot replicate. When the AI suggests a match, your domain knowledge asks: Was that road open in 1830? Would a widow have had the right to sell that land under the laws of that specific state? If the answer is no, the AI’s "discovery" belongs in the trash, not your tree.

Building Your "Domain" for the Future

If the AI is going to handle the "heavy lifting" of transcription and sorting, our job description is changing. We are being promoted from data gatherers to Architects of Context. To do this well, we have to intentionally build our "Domains":

• The Legal Domain: We must understand the dower rights and inheritance laws that governed our ancestors' lives.

• The Geographic Domain: We need to know the "Great Wagon Road" as well as we know our own neighborhood.

• The Technological Domain: We must learn how to "feed" our expertise into the AI, prompting it with the very context it lacks.

Moving Forward, Together

The theme for RootsTech 2026 is "Together," and I believe that applies to more than just our human kin. It represents the bridge between the machine's speed and the human's wisdom.

We aren't being replaced; we are being called to a higher standard of accuracy. When we bring our hard-earned domain knowledge to the table, we ensure that the stories we tell consistent with historical records. 

The "Aha!" Moment: A Case Study in Domain Knowledge

If you want to see another example of the difference between AI logic and human expertise, look no further than the "Same Name" trap.

In a recent research session, I asked an AI to analyze a specific family line. The AI confidently merged two different families because both fathers shared the same name. On the surface, the dates were "close enough" for a machine that thinks in probabilities.

But as a seasoned researcher, I saw the flaw immediately:

1. The Geography: The families lived in two distinct regions with no documented migration trail between them.

2. The Chronology: The birth dates of the children overlapped in a way that defied biological reality.

        Issues such as this one can be addressed by two rules or methodologies.

• The "Trust but Verify" Rule: Never accept an AI's "Match" without running it through your own geographic and chronological filters.

• The "Correction Loop": Use your domain knowledge to find an error, but don't give up on the tool. Instead, you should "teach" the AI by providing the specific domain knowledge it missed (e.g., "These are two different men because the 1850 census shows them in different states simultaneously").

When we correct an AI chatbot, we aren't just fixing a mistake in our tree; we are practicing the exact type of critical thinking that defines a master genealogist. We are proving that while the machine can find the data, only the human can find the truth."