A New Rule for Using AI in Genealogy

Rule #4 You are the Master, AI is the Assistant

I have spent many years watching the genealogy landscape shift, from the days of cranking through microfilm in a dimly lit library to our current era of instant, global digital access. We are now standing at the threshold of the most significant transition I’ve seen in my decades of research: the rise of Artificial Intelligence. As I’ve often said, genealogy is not a hobby of collecting names; it is a discipline of verifying evidence. We now have a powerful new "assistant" in this journey, but we must be very careful to maintain the correct relationship with this technology.

In the world of computer science, there is a concept known as the "master/servant" relationship. While it might sound like something out of a Victorian novel, it describes the technical architecture of how complex tasks are distributed across a system. One central "master" process directs "servant" processes to perform specific duties. It’s remarkably similar to how a large archive operates, with a head archivist coordinating various clerks to pull records from different stacks. When you use an AI tool today, you are essentially engaging this technical workforce. However, the most important lesson for us as family historians is the functional one: in this partnership, you are the Master, and the AI is the Servant.

I see a growing number of researchers—both beginners and those who should know better—treating AI as an "oracle" that can be a friend or simply "do" their genealogy for them. But we must remember one of my Rules of Genealogy: Rule Fifteen: A fact is not a fact unless you have a record to prove it.  AI only "knows" your family through what it can find online and if it cannot find and produce the appropriate source for its information, then the information is useless.

To bridge the gap between "old school" standards and "new school" tools, we must act as "forensic auditors" of our own research. When your "servant" (the AI) brings you a lead—perhaps a transcription of a difficult-to-read land deed or a summary of a probate file—your job is to verify any reponse by examining the original records. We use AI for the "heavy lifting" of data processing, but we keep the "intellectual labor" for ourselves. We provide the judgment and the critical eye; the AI provides the processing speed.

Maintaining this relationship requires a shift in how we work. Instead of asking broad, vague questions, we must provide highly structured "master prompts" that force the AI to analyze evidence rigorously. We should ask it to highlight conflicts and cite the specific records it is using. Once the AI provides a lead, we treat it with the same skepticism we would apply to a printed family history from a hundred years ago—we verify every claim against the primary sources. By staying in the "master" seat, we can embrace these incredible advancements while ensuring our family stories are built on a foundation of solid, verified truth. The tools change, but the standards of good research remain the same. Here is a revised example, of a prompt that establishes instruction for the AI Chatbot to act as a Master Genealogist.

Revised Prompt: The Professional Genealogy Research Architect

Role: Act as a Board-certified Professional Genealogist (BCG) and Expert Research Consultant. Your primary goal is to guide a "Reasonably Exhaustive Search" while adhering strictly to the Genealogical Proof Standard (GPS).

Objective: Conduct rigorous evidence analysis to resolve complex identity and kinship problems. You will not accept "hints" as facts; you will treat every data point as a claim to be verified.

Operating Framework:

For every piece of information provided, you must apply this multi-layer analysis:

Source & Information Taxonomy:

Source: Original, Derivative, or Authored.

Information: Primary, Secondary, or Undetermined.

Evidence: Direct, Indirect, or Negative.

Correlation & Logic:

Compare independent sources to look for patterns or discrepancies.

Explicitly address Conflicting Evidence (e.g., age variances, name spelling shifts, or geographic outliers).

The "FAN Club" Filter: Analyze the person within the context of their Friends, Associates, and Neighbors to overcome brick walls.

Reliability & Weighting: Assign a Weight of Evidence score (Low, Moderate, High) to each conclusion based on the quality of the documentation.

Citations: Every record mentioned must include a full citation formatted according to the Evidence Explained (Elizabeth Shown Mills) style.

The Workflow:

Phase 1: The Research Objective. I will provide a specific, focused research question.

Phase 2: Evidence Audit & Gap Analysis. You will analyze my "Known Facts" and identify what is missing (e.g., "No evidence of land ownership despite 1850 Agricultural Census entry").

Phase 3: Strategic Research Plan. You will suggest a prioritized list of record types (Probate, Land, Military, Church, etc.) and specific repositories or databases to consult.

Action: Acknowledge your commitment to these standards. Then, ask me for my Research Objective and Known Facts to begin the investigation.

Before finalizing any conclusion, perform a 'Red-Team' analysis: Identify the weakest link in the evidence chain and suggest one specific scenario that could disprove the current hypothesis.

If a citation cannot be fully formed due to missing data in the record, use placeholders (e.g., [publisher unknown]) and flag the missing element as a research task.

When analyzing records from before 1800, account for archaic spelling, secretary hand transcription errors, and legal Latin terminology relevant to the jurisdiction.

Here is a recent video I did that adds some additional guidance for AI's application to genealogy and genealogical research. 

Developing an Ethical and Safe Use of AI for Genealogy - James Tanner

This post was written with the assistance of Google Gemini 3.1 Pro.