I have not used the high-end Artificial Intelligence tools that require a subscription, but I want to stay informed about them and how they can enhance, improve and amplify genealogy and family history work.
Recently, I asked OpenAI ChatGPT5 (the free version) to "Please describe complex use cases that ChatGPT Work could
perform for genealogists and family historians."
(AI Google Gemini Notebook Inbfographic - Complex Genealogy Use Cases for ChatGPT Work)
The ChatGPT5 response was (split into two parts - this is Part 1) is:
The big difference is that ordinary ChatGPT answers a
genealogy question; ChatGPT Work can take on a genealogy project—researching,
navigating websites, handling files, comparing evidence, producing a
finished artifact, and continuing through many steps.
OpenAI
describes Work as an agent that can work across apps, files, and the
web and stay with complex projects for hours.
For a serious family historian, that opens up some pretty
interesting possibilities.
1. A complete “brick-wall investigation”
Imagine giving Work this assignment:
“Investigate the parentage of Devier James Lamphier
Smith, born about 1839 in Jefferson County, New York. Use my research
files, my family tree, census records, probate, land records,
newspapers, court records, and DNA evidence. Identify every plausible
parentage hypothesis, test each one against the evidence, document
negative evidence, and produce a research report with citations.”
Work could potentially:
- Search your genealogy files for
everything you've already discovered.
- Examine your tree and existing
notes.
- Search relevant online
repositories.
- Open record images and
transcriptions.
- Build a chronological timeline.
- Compare households in the 1830 and
1840 censuses.
- Investigate the Lanfear families
in Jefferson County.
- Examine land transactions and
probate.
- Compare the evidence against your
DNA-match hypotheses.
- Keep track of which sources
support or contradict each hypothesis.
- Produce a research report rather than merely a pile
of search results.
That's a fundamentally different workflow from asking, “Who were
Devier's parents?”
2. “Research what I already have before
searching”
This may actually be one of the most useful applications for you.
You have decades of accumulated genealogy material. Work could be
given a folder containing:
- genealogy sketches
- PDFs
- census images
- obituaries
- correspondence
- research notes
- newspaper articles
- photographs
- RootsMagic reports
- previous blog posts
- DNA research notes
Then:
“Before doing any new research, determine everything I
already know about Sarah Fletcher Kemp. Organize it chronologically,
identify contradictions, identify missing evidence, and list research
questions.”
Work can work across files and connected file services rather than
treating each document as an isolated upload.
For someone with a 75,000+ person tree and thousands of
documents, this could be enormously valuable.
3. An automated Genealogical Proof Standard
investigation
You could give Work a person and say:
“Evaluate this person according to the Genealogical
Proof Standard. Separate original from derivative evidence, analyze
source quality, identify conflicting evidence, search for reasonably
exhaustive evidence, and tell me what additional research is needed.”
It could create something like:
- Research question: "Who were the parents of
Sarah Fletcher?"
- Evidence located:
- 1802 birth evidence
- 1820 census
- 1830 census
- marriage record
- children's records
- probate
- land records
- cemetery evidence
- newspaper evidence
- Evidence supporting hypothesis A
- Evidence supporting hypothesis B
- Evidence contradicting A
- Unresolved questions
- Next five searches
That's much closer to having a research assistant
than a chatbot.
4. Census reconstruction across multiple decades
Give Work a family and ask:
“Reconstruct this family's household from 1790 through
1880. Find every census household, identify probable members, compare
ages and birthplaces, identify neighboring households, and produce a
family timeline.”
It could work across multiple census years and websites, rather
than you manually opening dozens of pages.
Even better:
“Look at the households surrounding Henry Carringer in
1850, 1860 and 1870. Identify neighbors who may have been relatives,
associates, or migration companions.”
That gets into cluster research, which is where
genealogy becomes particularly interesting.
5. FAN-club research at scale
This is one I'd especially like for genealogy.
Instead of researching only: Henry Carringer
Work could research:
- Friends
- Associates
- Neighbors
- witnesses
- sponsors
- employers
- landlords
- business partners
- nearby families
- people appearing in probate
- people appearing in land transactions
Then ask:
“Determine whether any of these people provide evidence
about Henry Carringer's origins.”
Work could build a cluster map and identify
connections that you might otherwise overlook.
6. Land-record research
Land records are notoriously tedious.
A Work assignment could be:
“Find every land transaction involving Henry Carringer
in Mercer County, Pennsylvania between 1825 and 1855. Extract
grantor, grantee, dates, acreage, locations, witnesses, adjoining
landowners and consideration. Put everything into chronological order
and identify relationships.”
The result could be a spreadsheet such as:
Date
|
Grantor
|
Grantee
|
Acres
|
Location
|
Witnesses
|
Significance
|
1831
|
…
|
Henry
Carringer
|
40
|
Sandy Lake
|
…
|
First
appearance
|
1837
|
…
|
Henry
Carringer
|
25
|
…
|
…
|
Adjacent
tract
|
1849
|
Henry
Carringer
|
…
|
15
|
…
|
…
|
Possible
relocation
|
That turns hours of clerical work into an organized
research dataset.
7. Probate investigation
Give Work:
“Investigate the probate of John Smith of Jefferson
County, New York. Identify every person named in the probate
documents, classify each person's relationship to John if stated or
implied, and compare the names with the 1830 and 1840 census.”
It could potentially:
- locate the probate file
- download documents
- OCR handwritten pages where
possible
- transcribe them
- identify names
- compare names against your tree
- construct a relationship
hypothesis
- flag discrepancies
And importantly, you could tell it:
Do not infer a relationship unless the evidence
supports it.
That's exactly the sort of constraint that makes AI much more
useful for genealogy.
8. Newspaper mining
This one could become addictive.
Suppose you have 500 newspaper references to Seavers.
You could say:
“Search these newspapers for members of the Seaver
family from 1850–1920. Extract every article that mentions a
Seaver. Classify each as birth, marriage, death, accident,
employment, travel, legal matter, social event, military service,
property, or miscellaneous. Eliminate duplicates.”
Then:
“Identify events that are not currently represented in
my genealogy database.”
That's a very powerful gap-finding operation.
9. Automatically turning research into
source-based biographies
You could give Work:
- census records
- vital records
- newspapers
- military records
- land records
- photographs
- your genealogical sketch
and say:
“Write a 2,000-word biography of Isaac Seaver. Every
factual assertion must be traceable to one of the supplied sources.
Separate documented facts from reasonable historical context. Do not
invent dialogue or undocumented events.”
Then another instruction:
“Now create a second version written as a historical
narrative, clearly labeling reconstructed scenes as plausible rather
than documented.”
That would fit beautifully with your ancestor-story
workflow.
10. “Find the holes in my tree”
This is a particularly interesting Work project.
“Analyze my family tree for people who have a birth
event but no birth source, a marriage event but no marriage source, a
death event but no death source, or census gaps of ten years or
more.”
Then: “Rank these research tasks by the number of descendants
potentially affected.”
Not a ranking of people—rather, a research-priority list
based on objective criteria you specify.
Work could then attack those problems one at a time.
Part 2 will follow shortly with ten more examples.
The Google Gemini Notebook video for this topic is on my YouTube channel here, and also below.
In every complex genealogy use case on this list, there are questions like "how can this be done?" and "what information, and in what format, do I need to provide?"
A big challenge for many of these complex use cases is that records needed are behind a commercial website paywall (e.g., Ancestry, MyHeritage, Findmypast, American Ancestors). Some of those websites have explicit restrictions on any AI tool being used to use the website to find records and information. They require the subscription user to do the searches.
It is evident that ChatGPT Work, like Claude CoWork, relies on an ongoing dialogue between the user and the AI tool for many use cases, and requires access to the user's files that have the records to be analyzed.
Some of these ten use cases can be performed using the free version of ChatGPT; for instance, #9, "Automatically turning research into sourced-based biographies."
Other researchers have performed some of these use cases using ChatGPT Work, Claude CoWork, and other AI Tools.
I may write blog posts about some of these complex use cases to answer those questions about them.
Note that ChatGPT5 uses my own research names, dates and places as examples for these use cases.
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