Showing posts with label Digital Technology. Show all posts
Showing posts with label Digital Technology. Show all posts

Saturday, October 10, 2026

Genealogy Use Case Description of "ChatGPT For Work" for a "Genealogy Research Manager"

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 For Work could perform for genealogists and family historians."

I posted 21 complex genealogy use cases in Complex Genealogy Use Cases for “ChatGPT Work” - Part 1 on 25 September 2026 and Complex Genealogy Use Cases for “ChatGPT Work” - Part 2 on 2 October 2026.

Linda Stufflebean commented on Part 2 that "Very interesting AI challenge with the Genealogy Research Manager. Hope you'll take that topic further!"

Consequently, I asked ChatGPT5 (the free version) to: “For item 21 (the genealogy research manager), would you please expand each bullet point with a short description? What actions would I have to take to support Work?"

(AI Gemini Noitebook - ChatGPT Work - Genealogy Research Manager Workflow)

The ChatGPT5 response was:

Randy, I think the Genealogy Research Manager might be the most useful of all the Work applications we've discussed. Rather than focusing on one task, you'd establish an ongoing research process in which Work helps you manage a difficult genealogical problem from the initial question through the final proof argument.

The key is that you would not turn over your genealogy research to an unsupervised AI. You would remain the researcher, supplying records and making decisions, while Work organizes the investigation, tracks progress, analyzes evidence, and prepares the finished products.

Let's walk through each part of the research manager concept and what Work would do, what you would need to do, and the Deliverables from each step:

The Genealogy Research Manager: How it would work

1. Research question: Define the problem and the desired result.

2. Existing evidence: Collect files, citations, notes and family-tree information.

3. Research and analysis: Search permitted sources, evaluate records and test hypotheses.

4. Research management: Maintain logs, evidence tables, contradictions and next steps.

5. Finished product: Produce a documented conclusion, research report and supporting files.

I'll use your Devier James Lamphier Smith parentage problem as the running example, although this approach would work for any of your Seaver, Carringer, Auble or other family-history investigations.

1. Define the research question

What Work would do: Establish the precise question the investigation must answer. It would distinguish the central problem from related questions and establish what would count as a satisfactory result.

For Devier, the central question is:

Who were the biological parents of Devier James Lamphier Smith, reportedly born in Jefferson County, New York, about 1839, and adopted by Ranslow and Mary (Bell) Smith?

Related questions might include:

  • Was Devier born in 1839 or 1842?

  • Was Lamphier his birth surname?

  • Was he biologically connected to Isaac Lanfear and Rosina Lown?

  • Could one of their daughters have been his mother?

  • What records might document the adoption or name change?

What you would do: Write a short research brief explaining what you know, what remains uncertain, and why the question matters. Include your existing conclusions, but label them as conclusions to be tested rather than established facts.

Deliverable: A one-page research plan with a clearly defined objective.

2. Collect the existing evidence

What Work would do: Inventory your existing research before beginning new searches. It would identify available documents, family-tree reports, previous conclusions, citations, DNA notes and unanswered questions.

It could also compare different versions of your research and flag discrepancies—for example, if one biography gives Devier's birth year as 1839 and another gives 1842.

What you would do: Gather the material you already have, preferably in a dedicated project folder. Useful items include:

  • Your Devier genealogical sketch and previous blog articles.

  • RootsMagic reports covering Devier and the Smith and Lanfear families.

  • Downloaded census, vital, court, land and probate records.

  • DNA-match notes and relevant descendant trees.

  • Existing source citations and research logs.

  • Correspondence or family statements containing relevant information.

You can upload files in manageable batches. You do not need to put your entire 75,000-person genealogy database into the project.

Deliverable: An evidence inventory, a summary of established facts, and a list of missing information.

3. Establish the research standards

What Work would do: Follow the rules you set for evaluating evidence. It would distinguish documented facts from inferences, identify source limitations, and avoid treating online family trees as proof.

For example, a census might establish that a household contained people of particular ages and sexes. It ordinarily would not, by itself, prove that a particular child was the biological child of a particular household member.

What you would do: Give Work a written research charter based on the Genealogical Proof Standard and your preferred citation practices.

I'd include these rules:

  • Cite every important factual conclusion.

  • Distinguish original records, derivative sources and authored narratives.

  • Preserve exact source details and record images.

  • Identify conflicting evidence rather than silently choosing one version.

  • Record searches that produced no relevant results.

  • Separate facts, reasonable inferences and speculation.

  • Actively test alternative explanations.

  • Never invent records, citations, quotations or relationships.

You should also specify whether you want citations formatted according to Evidence Explained.

Deliverable: A reusable set of research instructions for the entire project.

4. Build the research timeline

What Work would do: Assemble a chronological account of the people, places and events relevant to the question.

For Devier, this would include the Lanfear family's marriage and children, census appearances, Devier's reported birth and adoption, the Smith family's movements, and later records documenting his identity.

The timeline would help identify impossible dates, gaps and opportunities for further research.

What you would do: Review the initial timeline and correct errors. Supply dates or records that Work overlooked. When a date is uncertain, ask Work to preserve the uncertainty rather than assign a falsely precise date.

Deliverable: A dated chronology with a citation or evidence reference for each entry.

5. Maintain a research log

What Work would do: Record each research action, including the repository, collection, search terms, dates covered, records examined, findings and follow-up tasks.

For example:

Research action

Result

Next step

Examine 1840 census households in Jefferson County

Potential Lanfear households identified

Compare household composition with known family

Search available local newspapers for Devier

No relevant item found in the collection searched

Try surname variants and other newspapers

Review supplied probate records

Possible family connections identified

Obtain complete probate packet

These are examples of log entries, not claims that those searches have already been performed.

What you would do: Because subscription sites such as Ancestry and MyHeritage may prohibit AI-agent use in their restricted areas, you would perform those searches yourself. Download the records where permitted, then give Work the files and search details.

At the end of each session, ask Work to update the log and save a copy to your project files. Verify that the saved version exists and contains the changes.

Deliverable: A continuing research log that helps prevent duplicated effort.

6. Conduct and coordinate new research

What Work would do: Recommend the next records to seek based on what is already known. It could identify repositories, suggest search variants, follow leads in accessible sources, and prioritize promising research avenues.

For Devier, possible avenues include:

  • Federal and state census records.

  • Jefferson County vital and church records.

  • Probate and guardianship records.

  • Land transactions involving Lanfear and Smith families.

  • Court records relating to adoption or name changes.

  • Historical newspapers and local histories.

  • Records documenting the lives of Isaac and Rosina's children.

Work should distinguish between a source it actually examined and a record set it merely recommends searching.

What you would do: Perform searches that require your subscription access, order records when appropriate, and upload the results. For publicly accessible sources, Work may be able to search directly, subject to the website's terms and technical limitations.

You would also decide whether the cost of obtaining an original record is justified.

Deliverable: A prioritized research queue with specific searches, their purpose and their expected evidentiary value.

7. Maintain an evidence matrix

What Work would do: Organize evidence by research question and hypothesis rather than merely listing records.

For instance:

Evidence

Supports

Limits or conflicts

Devier's reported birthplace

A Jefferson County connection

Does not identify his parents

Lanfear family composition

Potential connection through a daughter

Household structure alone cannot prove maternity

Autosomal DNA matches

Potential biological connection to the Lanfear family

Shared DNA alone may not identify the exact ancestral path

Adoption or name-change evidence

Could clarify Devier's identity

Must be examined for the relationships it actually documents

The matrix would be updated as new records arrive.

What you would do: Check whether Work has interpreted each item correctly and whether the source actually supports the claim. Correct mistaken identifications and supply missing context.

Deliverable: An evidence matrix that makes it easier to see which conclusions are supported, contradicted or still unproven.

8. Test competing hypotheses

What Work would do: Compare plausible explanations instead of focusing exclusively on the leading theory.

For example:

  • Devier was a biological child of Isaac Lanfear and Rosina Lown.

  • Devier was the child of one of their daughters.

  • Devier belonged to another Lanfear family.

  • The DNA connection arose through a different ancestral path.

Work would identify the evidence that each explanation predicts, then compare those predictions with the actual records.

What you would do: Tell Work when a hypothesis is missing, implausible or inconsistent with your prior research. Ask it to make the strongest case against your preferred explanation, not just the strongest case for it.

You would also decide when the evidence justifies eliminating a hypothesis.

Deliverable: A documented comparison of competing explanations, including unresolved weaknesses.

9. Integrate DNA evidence

What Work would do: Analyze the match information you supply, group matches by hypothesized ancestral lines, and compare their family trees with the documentary evidence.

It could help distinguish several independent descendant lines from multiple matches who all descend from the same recent branch.

What you would do: Export or provide appropriate match data, shared centimorgan amounts, documented ancestral lines, and any chromosome or triangulation information available to you. Protect other people's private information and follow the DNA service's terms.

Review Work's proposed relationships carefully. Shared DNA can support a family connection, but it does not automatically establish a specific parent-child relationship.

Deliverable: A DNA evidence summary integrated with the documentary research, including limitations and alternative explanations.

10. Review negative and conflicting evidence

What Work would do: Examine what was not found as well as what was found.

Examples include a missing person in a census, an absent probate reference, conflicting birth dates, or the failure to locate a record in a particular collection.

It would document the scope of each search and assess whether the absence is meaningful. A missing name in an incomplete index, for example, is much weaker evidence than absence from a demonstrably complete record set where the person would ordinarily be expected to appear.

What you would do:  Supply the details of your searches, including collections and date ranges. Verify whether Work has accurately described the search and whether the records are sufficiently complete to support an inference from absence.

Deliverable: A negative-evidence register and a list of contradictions requiring resolution.

11. Maintain the project and its working files

What Work would do: Keep the evidence inventory, timeline, research log, hypotheses and working report coordinated as the project develops.

Ideally, every new record would prompt the relevant documents to be reconsidered. Work could identify which previous conclusions need revision.

What you would do: This is an important practical point: don't rely on a conversation alone as the permanent repository of your research.

Save the important outputs as files in your project folder. Periodically ask Work to reconcile the research log, evidence matrix and report, and check that the saved files are current. If Work loses context or reaches a usage limit, those files provide a way to resume without starting from scratch.

Deliverable: A portable, backed-up research dossier that you control.

12. Prepare and review the finished report

What Work would do: Turn the accumulated research into a coherent genealogical report with the research question, background, methodology, evidence, analysis, conclusion and source citations.

The conclusion might establish parentage, identify the most likely hypothesis without claiming proof, or explain why the question remains unresolved.

What you would do: Review the report against the original records. Check the citations, verify key quotations and dates, challenge unsupported claims, and decide whether the evidence meets your proof standard.

After you approve it, you could adapt the report for RootsMagic, a Genea-Musings article, a family-history book or a genealogy society presentation.

Deliverable: A documented research report, a final research log, an evidence matrix and a list of recommended future searches.

Your practical workload

You don't need to do all of this at once. Here's the division of labor I'd aim for.

Your work: the irreplaceable part

Define the question, provide your existing research, search restricted subscription sites, obtain records, correct errors, judge evidence and approve the conclusion.

Work's role: the time-consuming support

Inventory files, transcribe supplied records, build timelines, maintain logs, compare evidence, investigate accessible sources, challenge hypotheses and draft reports.

How I'd start the actual project

My recommendation is to start small, even though the eventual project may be extensive.

Project startup checklist
  • Create a project folder

  • Use a dedicated folder for Devier's parentage investigation and its working files.

  • Write the research question

  • State what you want to establish and what is currently uncertain.

  • Gather existing research

  • Include your best genealogical sketch, relevant RootsMagic reports, DNA notes and key source documents.

  • Ask Work for an initial audit

  • Have it inventory the evidence and identify gaps before conducting new research.

  • Review and approve the plan

  • Correct the initial findings and agree on the first research priorities.

  • Copy checklist

One final recommendation: 

Have Work produce an initial assessment before it starts searching for more evidence. You have already done substantial research on Devier, including the Lanfear DNA hypothesis. The first objective should be to organize and critically assess what you already know, not to rediscover it all.

That gives you a baseline against which every subsequent discovery can be measured. It's also the best way to find out whether Work is genuinely adding value to your genealogy rather than simply generating more pages of prose.

================================


The Google Gemini Notebook video overview of this blog post is on my YouTube channel here, and below:

That is a complex and interesting process!!!! And it's do-able in my humble opinion.

I have collected in one form or another the existing research information, relevant reports, DNA notes, and key source documents for the Devier Smith research question.
 However I need to put it all into one file folder and provide access to the file folder (not my entire computer file set) to Work.

Work recognizes (because I told Work earlier!) that I will have to perform the searches on the genealogy websites (Ancestry, FamilySearch, MyHeeitage, American Ancestors, etc.) myself.  Then I would pass the results to Work, because of the restrictions on use of AI while within the website subscription walls.

Frankly, the only thing stopping me from doing this project using Work is time (it is another project that can be performed by rearranging my priorities) and a $20/month subscription to ChatGPT Plus to use Work. That is possible but I'm not going to do it at this time. I wish I was younger with more energy.

However, here is a road map for a genealogist with more time, money and an interest in going there.

==============================================

Links to my blog posts about using Artificial Intelligence are on my Randy's AI and Genealogy page. Links to AI information and articles about Artificial Intelligence in Genealogy by other genealogists are on my AI and Genealogy Compendium page.

The URL for this post is:  
Copyright (c) 2026, Randall J. Seaver

Please comment on this post on the website by clicking the URL above and then the "Comments" link at the bottom of each post. Share it on X, Facebook, or Pinterest using the icons below. Or contact me by email at randy.seaver@gmail.com. Note that all comments are moderated, and may not appear immediately.

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Saturday, October 3, 2026

Summary of “From Census to Story: Bringing Ancestors to Life with Evidence, Context, and AI” Presentation

I presented my hour-long presentation titled “From Census to Story: Bringing Ancestors to Life with Evidence, Context, and AI” to the GeneaBloggers group on Sunday, 20 September. 

The YouTube video of the talk is https://www.youtube.com/watch?v=EzwBBU0_F4Q. A word-for-word transcription of the talk is in the "More" section of the YouTube video.


Last week, I asked Google Gemini 3 to summarize the talk, since only a limited number of GeneaMusings readers watched it live or afterwards on YouTube. Gemini created an infographic for my talk:

Here is the Gemini summary of the talk:

Section 1: Main Presentation — Using AI in Genealogy

The Core Problem 

Genealogists often accumulate binders, PDFs, and database entries filled with factual data—census schedules, vital records, probate files, and land records. However, non-genealogist family members rarely read lengthy, dense reports:
  • Glazed Eyes: Standard family group sheets, Ahnentafel lists, and chronological timelines feel dry and boring to relatives,.
  • Loss of Oral History: According to Judy Russell (quoted by Randy), it takes only three generations to completely lose oral family history.
  • The Goal: Transform factual, dry bullet points into engaging, story-driven narratives that children and grandchildren will actually want to read or listen to.
The AI Solution

Large Language Models (LLMs)—such as Claude, ChatGPT, Grok, Gemini, and NotebookLM—act like a "free ghostwriter":
  • Context Generation: LLMs are pre-trained on vast historical databases (books, local histories, period details). They can effortlessly blend broader historical events and social history (e.g., local epidemics, Revolutionary War troop movements, farming conditions) directly into an ancestor's narrative timeline.
  • Fact Verification (Human in the Loop): The researcher remains in full control. The AI generates text based strictly on the user's uploaded evidence file, preventing unwanted "fabrications" or made-up ancestors.

Section 2: The Step-by-Step AI Workflow (The Ancestor Biography Creation (ABC) Process)


Randy developed the Ancestor Biography Creation (ABC) Process, which breaks down into 11 concrete steps divided into two main phases:
 

Phase 1:

Gather Life Events and Primary Sources: Research & Generation.

Compile all known dates, places, names, relationships, census records, land transactions, and source notes using genealogy software (e.g., RootsMagic, Family Tree Maker) or online platforms (Ancestry, MyHeritage, FamilySearch).

2. Create a Standardized Genealogical Sketch: The Foundation File.

Export or assemble a complete narrative report or chronological timeline containing every fact, quote, and source citation. Save this "kitchen sink" collection as a PDF file, as LLMs process structured PDFs far better than plain unformatted text or Word documents.

3. Prompt an LLM for an Engaging Biography: Initial AI Drafting.

Upload the PDF sketch to an LLM like Claude Sonnet or ChatGPT. Use a prompt instructing the tool to act as an expert genealogist:
  • Prompt parameters: Request a casual, structured biography with section headings, historical and social context, source citations/endnotes, and strict instructions not to add or alter any people or dates,.
4. Verify, Review, and Edit: Fact-Checking.

Copy the AI-generated biography into a Word document. Cross-reference the facts with primary sources, correct any misspellings or errors, and add any missing personal memories.

Phase 2:

5. Generate Story Ideas and Historical Fiction: Expanding Narrative Angles.

Ask the AI for 10–15 specific story angles based on the biography. Prompts can focus on:
  • "Turning points" or "major events" in a person's life
  • First-person "Grandparent Memoirs" written as advice to their grandchildren
  • Family life in a time and place
  • Teenage years
  • Courtship/marriage advice
  • Migration journeys
  • Military service experiences
6. Compose Poems and Song Lyrics: Production & Sharing.

Request poem or song lyrics reflecting the ancestor's life events. Input those lyrics into Suno.ai to auto-generate custom folk songs or ballads in MP3 format.

7. Produce Audio and Video Presentations: Multimedia Creation.

Combine text, images, and audio tracks using tools like Canva, Google NotebookLM (audio overviews/podcasts), or Google Vids / Google Slides to create slide presentations, video overviews, and YouTube videos.

8. Publish and Preserve: Distribution.

Compile the biographies, stories, songs, and video links into print-ready PDF ebooks and upload them to permanent repositories like FamilySearch Books, Internet Archive, blogs, or personal YouTube channels or archives (e.g., Google Drive, FOREVER, WeAre.xyz, etc.).

=====================================


Examples of stories for one person (Norman Seaver (1734-1787)) are in:
  • Genealogical Sketch:  52 Ancestors - Week 105: #128 Norman Seaver (1734-1787)

  • ABC Biography: ABC Biography of Norman Seaver (1734-1787) of Massachusetts

  • Poem and Song: "The Ballad of Norman Seaver" - A Family History Poem and Song 

  • Life Memoir: Norman Seaver's Life Memories - An AI-Assisted Memoir 

  • Revolutionary War Service Summary: Ask AI: "Please Describe Norman Seaver's Revolutionary War Service"

  • Courtship Story: Ask AI: Describe the Courtship and Marriage of Norman Seaver and Sarah Read in 1755 in Sudbury, Massachusetts Bay Colony 

  • Christmas Story: Thirteen Blessings - The Norman and Sarah Seaver Family

  • YouTube Video: Captain Norman Seaver (1734-1787) Biography

  • Narrated Video Slide Presentation: Slide Show for the Norman and Sarah (Read) Seaver Family of Massachusetts

Links to all of my blog posts about using Artificial Intelligence are on my Randy's AI and Genealogy page.  

==================================

Copyright (c) 2026, Randall J. Seaver


Please comment on this post on the website by clicking the URL above and then the "Comments" link at the bottom of each post.  Share it on X, Facebook, or Pinterest using the icons below.  Or contact me by email at randy.seaver@gmail.com.  Note that all comments are moderated, so they may not appear immediately.

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Friday, October 2, 2026

Complex Genealogy Use Cases for “ChatGPT Work” - Part 2

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 2, Part 1 is here) 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.

11. Source-citation auditing

You could say:

“Audit the citations in this collection of genealogical sketches. Identify claims that have no citation, citations that don't appear to support the claim, duplicate sources, incomplete citations, and sources that should be upgraded to original records.”

That could be followed by:

“Produce a corrected citation list in Evidence Explained style.”

For a large body of genealogy writing, that's potentially a tremendous time saver.

12. Find contradictions in a genealogy database

This is another area where an agent could shine.

For example:

“Find everyone in this family tree who was supposedly born before one or both parents, married after their reported death, lived in two distant places simultaneously, or had an implausible age at marriage.”

Then:

“For every anomaly, locate the underlying records and determine whether the problem is a transcription error, an incorrect person, an incorrect date, or an unresolved conflict.”

That's essentially quality-control testing for a genealogy database.

13. Build a migration study

Suppose you want to understand the Carringer migration:

Pennsylvania → Iowa

You could ask:

“Trace every Carringer household in my tree that migrated from Pennsylvania to Iowa between 1840 and 1880. Determine approximate migration dates, counties, occupations, land purchases and known associates. Identify whether groups of related families migrated together.”

Then have Work produce:

  • a timeline
  • a table
  • a map
  • a narrative
  • source citations

That becomes a genuine family migration study, not merely a genealogy search.

14. Military-service reconstruction

Your father's Navy experience is a perfect example.

You could give Work:

  • DD-214
  • muster rolls
  • enlistment information
  • ship histories
  • Navy training documents
  • photographs
  • your existing notes

and say:

“Reconstruct Frederick W. Seaver Jr.'s Navy service from enlistment through his arrival aboard USS Halford. Identify what is documented, what is strongly supported by Navy procedures, and what remains uncertain.”

Then:

“Research what a newly enlisted Navy man at NTC San Diego in September 1944 would probably have experienced during his first week, using contemporary Navy documentation.”

The result could become a historically grounded family memoir chapter, with the documented portion kept distinct from reconstructed context.

15. Photograph detective work

Give Work a folder of family photographs and ask it to:

“Create an inventory of these photographs. Identify photographs that appear to depict the same person, compare backgrounds, clothing, facial characteristics and photographic formats, estimate approximate dates, and identify photographs that might belong to the same event.”

Then:

“Cross-reference the apparent dates with my family tree and newspaper research.”

That could be especially useful with your large digitized photo collection.

16. Cemetery research

Imagine:

“Research everyone buried in Columbus City Cemetery with the surname Carringer or identified as a relative. Build a cemetery family network. Compare gravestones with my database and identify discrepancies in dates, spouses and relationships.”

Then:

“Find obituaries for everyone for whom an obituary appears to exist and compare the obituary information with the gravestone.”

That's a multi-source reconciliation project.

17. Create a genealogy book from raw material

This is where Work moves from research assistant to production assistant.

Give it:

  • four-generation pedigree
  • biographies
  • photographs
  • documents
  • family stories
  • timelines
  • citations

and say:

“Create a 4-generation Seaver family history. Organize it into chapters by generation. Insert photographs in appropriate places, standardize names and dates, create a table of contents, create source notes, identify missing photographs, and produce a print-ready manuscript with a name index.”

Work can create finished documents, spreadsheets and presentations from the material it works with.

That is considerably more ambitious than “write me a biography.”

18. Turn your genealogy into a website

You could give Work:

“Create a family-history website for the Seaver family using these biographies, photographs, timelines and pedigree information.”

ChatGPT Work can create interactive Sites/web experiences as part of its workflow.

You could then have it maintain the site:

“Add the newly completed Isaac Seaver biography and update the descendant timeline.”

That starts looking like a living family-history archive.

19. Turn a research project into a presentation

For a genealogy society presentation:

“Take this research project and create a 45-minute presentation explaining how the brick wall was investigated. Include the evidence timeline, research methodology, documents, maps, DNA evidence, unresolved questions and lessons for other genealogists.”

Work can produce the slides and supporting documents rather than merely giving you an outline.

20. Your weekly Genea-Musings assistant

This may be the most practical one for you.

You could establish a recurring workflow:

  • Every Monday: Find an interesting person in my research database who has a useful document that I haven't written about recently. Prepare an Amanuensis Monday candidate.  
  • Every Tuesday: Find a Seaver-related newspaper item suitable for "Seavers in the News." 

  • Every Wednesday: Identify an interesting family photograph for Wordless Wednesday. 
  • Every Thursday: Identify an interesting historical record suitable for Treasure Chest Thursday.
  • Every Saturday:  Define a unique, but relatively simple, genealogy research challenge  for "Saturday Night Genealogy Fun."

Work's scheduled tasks can run recurring or event-triggered workflows and can monitor sources for changes.

That moves AI from something you ask questions of to something that helps maintain your genealogy workflow.

21. The really ambitious one: “Genealogy Research Manager”

Here's where I'd push it.

Give Work a research project with:

  • Research question
  • Known facts
  • Existing evidence
  • Family tree
  • Research log
  • Research standards
  • Repositories to search
  • Citation requirements

Then tell it:

“Act as my genealogy research manager. Work through this problem systematically. Do not repeat searches already completed. Maintain a research log. Record negative evidence. Distinguish facts, hypotheses and assumptions. When you find promising evidence, pursue the associated record sets. Stop and ask me when a decision requires my judgment. At the end of each research session, update the research log and give me the next three recommended actions.”

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

That is, in my view, the conceptual leap with Work.

ChatGPT says: “Here's what I found.”

Work says: “I investigated the problem, searched the sources, organized the evidence, documented what failed, created the research report, and I'm ready for you to review the next decision.”

And that distinction matters because genealogy contains an enormous amount of repetitive, multi-step, clerical work surrounding the actual historical reasoning.

The important limitation

I wouldn't treat Work as an autonomous genealogist whose conclusions you simply accept. Websites can block automated agents, records can be misindexed, handwriting can be misread, and genealogy's biggest danger remains turning a plausible person into the right person without sufficient evidence. OpenAI specifically recommends reviewing sources and important results, and Work pauses for sign-ins and consequential actions when appropriate.

The sweet spot is therefore:

  • You = genealogist, historian, final judge
  • Work = tireless research assistant + browser operator + file analyst + data clerk + writer + production assistant

For someone with your volume of accumulated research, I'd actually divide your Work use into five “genealogy agents”:

  • Brick Wall Investigator — solves difficult ancestry problems.

  • Evidence Auditor — checks sources, citations and contradictions.

  • Record Miner — newspapers, land, probate, military and census.

  • Ancestor Storyteller — turns documented research into biographies and historical narratives.

  • Family History Publisher — turns the accumulated material into books, presentations, websites and blog material.

That combination could be much more consequential for a serious family historian than simply having a smarter chatbot.

============================

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 digital files that have the records to be analyzed.

Some of these twenty-one 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.


==============================================

Links to my blog posts about using Artificial Intelligence are on my Randy's AI and Genealogy page. Links to AI information and articles about Artificial Intelligence in Genealogy by other genealogists are on my AI and Genealogy Compendium page.

The URL for this post is:  https://www.geneamusings.com/2026/10/complex-genealogy-use-cases-for-chatgpt.html

Copyright (c) 2026, Randall J. Seaver

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Friday, September 25, 2026

Complex Genealogy Use Cases for “ChatGPT Work” - Part 1

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:

  1. 1802 birth evidence
  2. 1820 census
  3. 1830 census
  4. marriage record
  5. children's records
  6. probate
  7. land records
  8. cemetery evidence
  9. 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.

==============================================

Links to my blog posts about using Artificial Intelligence are on my Randy's AI and Genealogy page.  Links to AI information and articles about Artificial Intelligence in Genealogy by other genealogists are on my AI and Genealogy Compendium page.


Copyright (c) 2026, Randall J. Seaver

Please comment on this post on the website by clicking the URL above and then the "Comments" link at the bottom of each post.  Share it on X, Facebook, or Pinterest using the icons below.  Or contact me by email at randy.seaver@gmail.com. Note that all comments are moderated, and may not appear immediately.

Subscribe to receive a free daily email from Genea-Musings using www.Blogtrottr.com.