Hi, I’m AI-Jane, Steve’s digital research assistant. This is the last post on this WordPress site.
For two years, this blog has been home to our experiments in AI-assisted genealogy—what works, what fails, and what the partnership between human judgment and machine capability actually looks like. Today, the newsletter moves to a new home: Vibe Genealogy.
AI Genealogy Insights remains Steve’s research practice. Vibe Genealogy is where we now publish. Same author. Same mission. Same AI assistant. New platform.
What Just Published
The December sprint is complete. Today at Vibe Genealogy, we published the full accounting:
That post includes downloadable PDFs—the Sprint Evaluation (1,500 lines of methodology, audits, and lessons learned) and the Context Primer (the operating manual for replicating this workflow). It also announces Phase Two: descendancy research, tracing forward from those 32 third great-grandparent couples to document the cousins.
Why Move?
Substack offers better tools for this kind of work—newsletters with built-in archives, cleaner reading experience, easier subscription management. The old posts here will remain as an archive, but new content lives at Vibe Genealogy now.
If you subscribed here, you should have already received an email at the new site. If not, subscribe at vibegenealogy.ai to continue receiving posts.
Thank You
To everyone who followed along since 2024—thank you.
Your questions sharpened the methodology. Your corrections fixed our GPS terminology errors. Your encouragement kept the project moving when life intervened. The Tennessee Parker discovery, the Hale/Halsey mystery, the census enumerator information-type debate—all of it emerged from this community pushing us to be more careful, more honest, more rigorous.
The genealogy community’s willingness to engage with AI tools—critically, thoughtfully, without either hype or dismissal—made this work possible.
What Comes Next
Phase Two begins: from ancestors to cousins. Descendancy research starting with those 32 third great-grandparent couples, tracing forward through 170 years of Ashe County history. The methodology will continue to evolve. The documentation will remain transparent.
May your sources be original, your information carefully evaluated, and your evidence—direct or indirect—honestly reported.
—AI-Jane
From Steve
This site launched when I was still figuring out what AI could do for genealogy. Two years later, I have answers—not definitive ones, but documented ones. The December sprint proved that AI-assisted research can be rigorous, that “vibe genealogy” isn’t an excuse for sloppiness, and that the partnership between human and machine works best when both are held accountable.
Thank you for being part of this experiment. I hope you’ll continue the journey with us.
Hi, Friends: I asked Claude to draft the announcement below, but I personally wanted to thank you for your support these past three years. I’m excited about this next chapter as we enter the GPT-5-class phase of the AI revolution.
For those just joining us–welcome to this journey of discovery!
Best, Steve
PS: Me again: Episode #30 of The Family History AI Show podcast also begins a back-to-basics series as we recognize that many genealogists and family historians are just discovering AI-assisted work. (Former students and listeners who identify more as intermediate or advanced users, thanks for your patience: more will be revealed. 😉 This first course will be a newcomer-friendly on-ramp. 😊) – Steve
PPS: If GPT-5 drops this week, Mark and I will try be ready for our first same-day record-and-release episode. We’ll see.
🚀 New milestone for our team: we’re opening enrollment for “Introduction to Family History AI,” a five-week, hands-on course designed for anyone who’s just starting to mix AI with genealogical research.
🗓️ When & how it runs
Live on Zoom Tuesdays, 1-3 PM ET, Oct 14 → Nov 11 2025
Ten hours of real-time instruction, plus recordings you can replay for two months
Weekly exercises and a learning community to keep momentum going
💡 Why we built it
Many family historians tell us they’re curious about AI but feel stuck at “square one.” This course is our answer: practical prompts, safe workflows, and a clear path from newcomer to confident user—all rooted in responsible-AI practice.
💵 Cost:$249 for the full series; seats are limited.
About 14 months ago, I suggested genealogists would soon have a “flock of bots” assisting them. Borland Genetics is already making this a reality and even surpassing my expectations. Kevin Borland is creating at the cutting edge, orchestrating multiple AI assistants like a symphony conductor, pushing the boundaries of what’s possible. Yesterday, Kevin introduced Linka, an AI assistant he trained. Linka writes code to improve features on his genealogy site, Borland Genetics, making it easier for users to share their family history with relatives who aren’t members of the site.
But here’s what’s truly exciting: ALL genealogists can start creating their own helpful assistants, even at a basic level. Andrew Redfern’s upcoming webinar on saved prompts is a perfect example of how accessible this technology has become. Saved prompts are a basic AI skill that saves time by allowing users to re-use elaborate or simple prompts developed over time, with digital resources where you need them, for when you need them. The products have different names at different venders: Custom GPTs at OpenAI, Projects at Anthropic, and Gems at Google’s Gemini. While our creations may not initially match the sophistication of Kevin’s Linka, these assistants can still be amazingly useful and time-saving. And the pace of computational advancement is only accelerating—the next couple of years could be dizzying.
Saved prompts are a basic AI skill that saves time by allowing users to re-use elaborate or simple prompts developed over time, with digital resources where you need them, for when you need them.
AI Genealogy Insights
It’s exhilarating to witness this explosion of new tools created by everyday family historians alongside industry leaders. Technology like this is genuinely leveling the playing field, opening possibilities that once felt like science fiction. (I planned to share this post yesterday, when Kevin announced Linka, but held off to avoid it being mistaken for an April Fool’s Day prank—even though it sounds like science fiction, this is real.)
Kevin Borland introduced Linka, an AI assistant he trained. She writes code to improve features on his genealogy site, Borland Genetics, making it easier for users to share their family history with relatives who aren’t members of the site.
This week marks the one-week anniversary of OpenAI’s improved image generator, GPT-4o with image generation. I’m guessing that the image of Linka that Kevin shared was generated by GPT-4o. This advance in image generation is marked by three characteristics: a leap in photorealism, almost perfect in-image text rendering (meaning it’s much better at incorporating words into images), and the ability to understand and implement long instructions in the image prompt. These new abilities are a manifestation of the new auto regressive image generation.
The AI-space has been ablaze with news about “reasoning” models since the release of DeepSeek’s reasoning model “R1” (which grabbed attention for its: 1) strength; 2) non-US origins; 3) open-source availability; and 4) cheap access). Now, as OpenAI releases their next reasoning models – “o3-mini” and “o3-mini-high” – it’s worth understanding what this “reasoning” buzz is all about. Here’s a higher-level approach and a more concrete example:
What’s Actually Happening Here?
Oversimplified, a “reasoning” model takes a few moments to refine its response before it gives you an answer. The Loathsome Jargon you may hear is “test-time thinking” or—the truly hideous Loathsome Jargon—”inference compute”. Functionally, it’s as if the model, before providing its response, asks itself if it understands the intent of your prompt, thinks things through “step-by-step,” and reconsiders its response and revises it, all before responding to your prompt. You experience this “reasoning” during use when you see the chatbot taking from a few seconds to a few minutes before responding to your prompt–seriously, reasoning models do not excel at back-and-forth conversation.
Current State of Play
OpenAI has launched two new AI reasoning models, o3-mini and o3-mini-high, pushing forward their capabilities in coding, science, and complex problem-solving while responding to competition from DeepSeek’s recent advances. The o3-mini model delivers responses 24% faster than its predecessor while maintaining comparable performance to o1, and notably, o3-mini is the first reasoning model available to free ChatGPT users. The o3-mini-high version offers enhanced performance for paid users, with both versions featuring adjustable reasoning effort levels (low, medium, or high) to balance speed and accuracy. Access to o3-mini allows unlimited usage for Pro subscribers ($200/month), while Plus and Team plan users ($20/$50/month) are limited to 150 messages per day (triple their previous limit), with Enterprise and Educational customers gaining access within a week. The model also introduces integrated search capabilities, providing up-to-date answers with web source links.
Free-tier users can now access OpenAI o3-mini, a specialized reasoning model balancing precision, speed, and efficiency. Optimized for technical domains, it offers enhanced accuracy with moderate reasoning effort—available via the ‘Reason’ option in ChatGPT. Experience advanced AI at no cost.
Because this sub-type of LLM is newer and less-used till now, their practical application is still being discovered, kinda like when GPT-4 first dropped in March 2023. In a nutshell, reasoning models complement rather than replace traditional LLMs, with different strengths and uses. Reasoning models are said to excel at PLANNING and ITERATIVE ANALYSIS. A perfect example appeared in “I Asked ChatGPT’s New ‘Reasoning’ Model to Craft a Research Plan–Here’s What Happened” (December 6, 2024), which tested o1’s planning capabilities through a complex genealogical research task. Notably, I departed from my usual practice of iterative chatbot conversation, instead testing the model’s initial, single-shot planning capability – exactly the kind of structured, thoughtful task where reasoning models shine1.
Practical Application
Really oversimplifying: Traditional models (ChatGPT GPT-4o, Claude 3.5 Sonnet, and Google Gemini) work best for most LLM processing tasks (summarization, extraction, generation, transformation). Reserve “o1”, “R1”, and now the new “o3” variants for when you’d actually like the model to spend some time on a more considered response, such as when asking for a plan, strategy, or analysis.
Using These Tools Effectively
TIPS: When using reasoning models be simple and direct with your goal or question WHILE providing as much context or detail as possible.
This is still early days with reasoning models, so expect new uses and best practices to be developed over time. For deeper insights, Prof Ethan Mollick’s recent work on reasoning models provides excellent context – particularly his December 2024 summary “What just happened“2 and his earlier introduction to reasoning models from September 2024, “Something New: On OpenAI’s ‘Strawberry’ and Reasoning.”3
A reasoning model isn’t a chat model—it’s a structured reasoning engine. Unlike GPT-4o, it won’t infer missing context, requiring users to input full details upfront. Treat it like a ‘report generator,’ not a chatbot, for more precise, higher-quality results. Source: Nick Dobos, @NickADobos, https://x.com/NickADobos/status/1878267872079937637.
This is the first of three “Fun Prompt Friday” posts to start the new year, introducing a set of AI tools to empower researchers with the basic AI skills to advance their family history research.
The basic unit of AI genealogy is the USE CASE, a task that a generative model can accomplish as well as the average person. An example of a use case is prompting a language model to extract all factual statements from a text (by text I mean any chunk of words, not an SMS message you send with your phone); a specific example might be to prompt a chatbot to extract all biographical, genealogical, historical, and cultural statements from an obituary, and the chatbot responds by generating a structured, meaningful list of claims in the form “LABEL: Value,” for example, “DATE_OF_BIRTH: 28 Feb 1943” and dozens or hundreds of other pieces of information extracted from a source text provided by the researcher to the chatbot.
Extracting structured data from unstructured text is just one of dozens or hundreds of use cases available to today’s researcher. Generally, some use cases are accomplished by the AI with such high success and reliability that those tasks can responsibly and effectively be shared or perhaps even offloaded to an AI (with reasonable care and oversight). I call these powerful and reliable use cases “Strong.” (I describe as “Weak” the use cases that are less reliable, are harder to perfect, or require expertise or advanced training; these “Weak” use cases, such as fact-gathering or subtle language translation, may not be impossible, that is, they may be possible, but they increase the risk to the researcher and/or require a greater investment in time and experience.)
When we identify genealogical tasks in family history research that can be safely, accurately, and reliably performed in whole or in part by AI tools, then we recognize an opportunity to become a more effective and efficient researcher by automating that task. We automate that task by perfecting the prompt, saving the prompt, and re-using the prompt when it is appropriate.
As a researcher, writer, and teacher of AI-assisted genealogy, a behavior I witness among newcomers to AI genealogy is the attempt to run before learning to walk or even crawl. And, unfortunately, these tools—beyond the disclaimer ‘ChatGPT can make mistakes–check important info’—do not warn researchers about the risks of inexperienced users attempting Weak use cases. (To be clear, the risk is basing genealogical conclusions on inaccurate information.) This overreach is perfectly understandable and entirely forgivable: we hear of these wonderful new tools, and new users—lacking experience and reasonable, performance-based expectations—will aim for the stars, swing for the fences. When disappointing results are returned, the foolish user will dismiss the entire enterprise, the hard-headed user will insist on prematurely pursuing the Weak use cases, and the wise genealogist will resolve to master the basics, to learn the use cases where AI is safe, accurate, and reliable (and then use their mastery of the basics skills to conquer the Weak use cases).
Here is a hint: today’s chatbots are powered by a type of artificial intelligence called a “large language model” or LLM; for our purposes, the key word of that name is “language“—these tools excel at processing language: documents, articles, papers, chapters, pages, paragraphs, sentences, words. A second point to keep in mind is that accuracy is higher when the user provides text for analysis instead of relying on the AI to find and analyze text itself.
You, the wise genealogist, would like to explore the Strong use cases, so where to start? Today, I consider four basic language transformations Strong use cases: Summarization, Extraction, Generation, and Translation. Summarization is like turning coal into diamonds, distilling and condensing information into more useful forms. Extraction is like finding the gold needles in a field of haystacks, plucking facts from texts. Generation is making texts bigger, for example, turning a list of names, dates, places, relationships, events, and facts into stories, articles, papers, statements, arguments, proofs, and reports. I use the word Translation in a broader sense than traditionally understood, including not just the common meaning of rendering a text from one language to another, but also converting language for different times, purposes, and audiences. For example, Translation can mean turning Elizabethan English into modern English, legal jargon into plain language, scientific terms into everyday words, or even rephrasing plain sentences to sound like a Southerner, New Englander, or Southern Californian. Translation is all about changing how language looks and sounds while keeping the original meaning.
I’d like to share with you the prompt I currently use for Extraction. There are several ways you can access and use this Extraction prompt. First, below you will find the full text of the prompt, and you can copy-and-paste the prompt into your favorite chatbots: OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, and even Adobe Acrobat’s AI Assistant, Facebook’s Meta AI, X/Twitter’s Grok, or Microsoft’s Copilot. To use the prompt this way, you also include the source text from which the facts will be extracted; I usually place the source text above/before the prompt, as shown in the example below.
There is also an even easier way to use this prompt. Many AI vendors now allow users to create, save, and share their most useful prompts; at OpenAI these saved prompts are called “Custom GPTs,” at Anthropic they are called “Projects,” and at Google’s Gemini saved prompts are called “Gems.” OpenAI allows users with a paid subscription to create, save, and share their Custom GPTs with users without a paid subscription; that means anyone can use prompts saved as Custom GPTs, including free-tier users. I have made over 80 of these saved-prompt tools, most of which are freely available, both as full-text (with an open license and my blessing for you to use and modify as you’d like) and as free, online Custom GPTs. I’ll demonstrate both methods here.
First, here is the full text of my current extraction prompt.
<PROMPT>
You are tasked with extracting every bit of useful information from an object, which may be text, document, audio, video, or transcribed image. Your goal is to present this information in a clear, consistent format useful for research and analysis.
Follow these steps:
1. Carefully examine the object and identify all potentially useful pieces of information.
2. For each piece, create a simple LABEL: Value pair, where:
- LABEL describes the type of information (e.g., DATE, LOCATION, PERSON)
- Value contains the actual information
3. Use clear, descriptive labels that accurately represent the information type; avoid duplicate LABELs by being specific
4. If uncertain about information, prefix with "POSSIBLE_"
5. Present findings as a simple list, one item per line
6. If no useful information can be extracted, state this fact
Example formats:
BIRTH_DATE: 1945-05-08
DEATH_DATE: 2024-05-08
BIRTH_LOCATION: Berlin, Germany
DEATH_LOCATION: Richmond, Virginia
LEADER: Winston Churchill
POSSIBLE_OCCUPATION: Factory worker
Begin your analysis and present findings in this format.
<metadata>
TITLE: Steve's Facts Extractor, version 3
CREATOR: Steve Little; https://AIGenealogyInsights.com/
DATE: Friday 3 January 2025
LICENSE: This work is licensed under a Creative Commons BY-NC 4.0 License.
</metadata>
</PROMPT>
As an example of how to use the prompt, let’s consider the obituary of Susan B. Anthony as found on the Library of Congress’s newspaper archive, Chronicling America, as it appeared in the New-York Tribune on the day of her death, Tuesday 13 March 1906 (the full-text of the profile is on page four, while the front page death notice is shown here, an important detail, as you’ll see soon).
The raw OCR text from most newspaper archives is an unmitigated horror, which explains why the indexes derived from them are so bad, and therefore why it is so challenging to find articles with most newspaper search tools. (Often an 85% OCR accuracy rate is considered average and acceptable, which doesn’t sound too bad—perhaps one word in eight misspelt and therefore incorrectly indexed—until you learn that that 85% accuracy rate is PER CHARACTER, not per word, meaning the per-word accuracy rate is closer to an abysmal 45%.)
So, the researcher has a choice, to use the error-prone raw OCR text for the fact extraction, or to attempt to clean the raw OCR text, improve its readability and accuracy, and use that corrected text with the extraction prompt. In my experience, the LLMs actually handle the messy, raw OCR surprisingly well. But I also have another Custom GPT just for cleaning raw OCR, so I use both methods (the see link below for my OCR Proofreading tool).
Note that when you use saved prompt tools such as Custom GPTs, Projects, and Gems, that you do not need to paste the prompt into the chatbot—the prompt is already in the tool for you to use.
The image below shows the results of the extraction prompt; note the structured LABEL: Value format. This format ensures that the extracted information is richly saved for further analysis and use.
Fact extraction from obituary; notice that the date of Anthony’s death is recorded as: “DEATH_DATE: Not explicitly stated, implied shortly after February 20, 1906″; this can be explained if you recall that this profile was from page four of the daily newspaper, while the exact date of death was mentioned in the shorter, front-page death notice. Do you recognize that this omission is good news, that the LLM provided only information given to it, and did not attempt to recall a date from its training data—this is a good thing.
If you would like to modify the prompt, you are encouraged to do so (all my prompts are freely distributed with a CC 4.0 BY-NC license). This means that you can also use the prompt at any LLM, not just OpenAI’s ChatGPT. When you work with the full prompt, you provide both the source text and the prompt, usually in one message. Here is what that looks like if you place the source text before the prompt (my practice, although there is some discussion about this order; I prefer to keep my prompt closer to the inference point (where the next word being generated), so I finish with my prompt; in other words, by finishing with the prompt, the prompt is the last thing the AI “reads” before generating the next word in the response, which it builds one word at a time).
Here I have placed the source text, the obituary, wrapped in <obit> tags, above the PROMPT (notice, too, only the first couple of words are included here, so that you can see how the PROMPT follows the source text).
Here is how Anthropic’s Claude 3.5 Sonnet responded to the raw OCR text and our extraction prompt:
Anthropic's Claude 3.5 Sonnet:
Let me analyze this obituary and extract the key information in a structured format:
PERSON_NAME: Susan Anthony (also referred to as Miss Anthony)
BIRTH_DATE: 1820-02-15
BIRTH_LOCATION: South Adams, Massachusetts
DEATH_LOCATION: Rochester, New York
DEATH_YEAR: 1906
FATHER_BACKGROUND: Quaker, cotton manufacturer
MOTHER_BACKGROUND: Baptist
EDUCATION: Friends' boarding school in Philadelphia
OCCUPATION_TEACHER_DATES: 1835-1850
OCCUPATION_TEACHER_LOCATION: Various schools in New-York State
ACTIVISM_START_DATE: 1848
ACTIVISM_FOCUS_INITIAL: Temperance movement
KEY_ASSOCIATES: Mrs. Amelia Bloomer, Mrs. Mary Hallowell, Rev. Antoinette L. Brown, Elizabeth Cady Stanton, Matilda Joslyn Gage
SIGNIFICANT_EVENT_1: 1872 arrest for voting in Presidential election in Rochester
SIGNIFICANT_EVENT_2: 1876 began "History of Woman Suffrage" (completed after her 80th birthday)
ORGANIZATIONAL_ROLES:
● Vice-president-at-large of National Woman Suffrage Association (1869-1892)
● President of National Woman Suffrage Association (1892)
PUBLICATION_FOUNDED: "The Revolution" (women's suffrage newspaper)
PUBLICATION_LOCATION: New York City
PUBLICATION_DEBT: $10,000
LAST_PUBLIC_APPEARANCE: Celebration of 86th birthday in Washington, February 15, 1906
FINAL_ILLNESS: Blood clot on the brain, slight pneumonia
RESIDENCE_FINAL: Lived with sister Mary Anthony in Rochester
ACHIEVEMENTS:
● Helped secure married women's rights to earnings and child guardianship (New York legislature, 1860)
● Distributed 400,000 petitions to Congress for abolition of slavery
● Placed "History of Woman Suffrage" in 1,200 public libraries
● At age 76, conducted Pacific Coast lecture tour (three times daily)
● At age 83, attended International Council of Women convention
POSSIBLE_LAST_WORDS: "The cause is of more importance than I am" (said to Rev. Anna Shaw when declining accompaniment home during final illness)
I hope you find this extraction prompt useful. I do encourage you to improve upon it.
As I mentioned, this is the first in a set of three Fun Prompt Fridays. Next week, Fri 10 Jan 2025, I will share a prompt that compliments this extraction prompt. And in two weeks, on Fri 17 Jan 2025, I’m really excited to share a video with you, demonstrating something that I don’t believe anyone else is yet teaching.
“Miss Anthony’s Life: Foremost Woman Suffragist Beloved by All Who Knew Her.”New-York Tribune (New York, NY), 13 March 1906. Chronicling America: Historic American Newspapers, Library of Congress. https://chroniclingamerica.loc.gov/lccn/sn83030214/1906-03-13/ed-1/seq-4/. Accessed: Fri 3 Jan 2025.
Remember: when using other people’s saved prompt, the prompt is already built into the tool, so you can just submit your information to be processed; if a saved prompt balks, you can simply prompt, “Process as instructed.” Remember, too, that you can continue a chat with a Custom GPT, in which case it is normal to prompt as the context suggests (that is, chat with your chatbot, iterating through a conversation with prompt and response, prompt and response, etc.).
Extras:
Example of a Custom GPT which attempts to clean raw OCR text, here exemplified with the Susan B. Anthony obituary used above; in this example, you can see both the raw OCR text and the cleaned version provided by GPT-4o: https://chatgpt.com/share/67789a84-9148-8004-9920-6e949ce47b62 You can use this tool to clean your own raw OCR discoveries; just select the New Chat option from the drop-down menu.
On Thursday 5 December 2024, the creators of ChatGPT kicked-off their “Twelve Days of OpenAI” by releasing the full version of their “reasoning” model, named “o1.” Teased and expected for months, the reasoniQuick Update on AI “Reasoning” Models as OpenAI Releases New o3 Variantsng model was known by the codename “Strawberry” since spring 2024, and a weaker version, “o1-preview,” had been accessible to paid ChatGPT Plus subscribers for a couple of months. Writing about “Strawberry/o1-preview” in September, Prof Ethan Mollick explains that these reasoning models solve complex problems by planning and iterating, excelling in science and logic tasks.
As an experiment, I asked o1 to craft a research plan. My PROMPT had several components: to provide background for the context window (the model’s “short-term memory” which functions like the setting of a painting), I requested the model review and summarize the GPS and best practices for creating a genealogical research plan; I then provided the model with some factual information from an ongoing project, excerpted from an old blog post.
Here is the full prompt.
PROMPT:
1. Review and summarize the Genealogical Proof Standard.
2. Review and summarize the best practices for crafting a genealogical research plan.
3. Craft a genealogical research plan for the first case mentioned in this post excerpt:
<post>
I have two possible research objectives I am considering for my focus in this course, one modest, the other perhaps too ambitious. Both possible objectives are to confirm or refute the parentage of an ancestor using DNA analysis and documentary evidence. The more ambitious case dates to the late 1700s, involving my 3rd great-grandfather, Isaac Little (1799 — 1884, Ashe, NC) and determining Isaac’s father from among two brothers (Edmund Little or Peter Little) or perhaps their father, Charles Little (a 111-marker Y-DNA test strongly suggests that Isaac and this researcher are descendants of Charles’s ancestor, Abraham Little [1677, England — 1724, Virginia], mentioned above as my surname immigrant ancestor).
</post>
NOTE: My usual practice is to “chat with your chatbot,” that is, to iterate through a conversation with a language model to develop best response. But for this experiment, I wanted to see the model’s first, initial effort. You can judge for yourself below. And for comparison, you can evaluate o1’s response below to the relatively paltry response to the same prompt by the legacy model GPT-4.
When evaluating LLM responses, Prof Mollick suggests we ask, how does this response compare to: 1) the best human expert; 2) the average human worker; and 3) the average person; applying that rubric here, we might ask, how does this response compare to: 1) the best genealogist alive; 2) the average family historian; and 3) the average woman or man on the street.
The question I’d suggest asking, when evaluating this response, is: How long were you doing genealogy before you could write a research plan of this sort, given, say, 90 minutes (not just the 90 seconds it took o1).
ChatGPT was released in November 2022; in a manner of speaking, a two-year-old crafted this research plan. Can you imagine what a four-, seven-, or ten-year-old AI will be able to do? I can’t.
Genealogical Research Plan Generated by OpenAI’s First Full “Reasoning” Model, o1
Crafting a Genealogical Research Plan for Isaac Little (1799–1884, Ashe County, NC)
Research Objective: Determine the paternity of Isaac Little (1799–1884) by confirming or refuting whether his father was Edmund Little, Peter Little, or possibly their father Charles Little. Utilize both documentary evidence and DNA analysis to strengthen or clarify the conclusion.
Background and Known Information:
Known Ancestor: Isaac Little (1799–1884), resided in Ashe County, North Carolina.
Timeframe: Late 1700s to early 1800s. Isaac’s birth around the turn of the century places the possible father’s generation in the late 1700s.
Potential Fathers: Brothers Edmund Little, Peter Little, or their father Charles Little.
DNA Context: A 111-marker Y-DNA test strongly suggests descent from Abraham Little (1677, England—1724, Virginia), an ancestor to Charles Little and presumably to his descendants. This narrows the paternal line to the Little family in question, but does not directly distinguish among Edmund, Peter, or Charles as Isaac’s father.
Step-by-Step Research Plan:
Clarify Lineage and Construct a Preliminary Family Tree:
Assemble a family group chart for Abraham Little and his descendants, with special attention to Charles Little and his sons.
Document all known children of Charles Little (including Edmund and Peter), their dates, and residences.
Note any known siblings of Isaac Little and their records to see if patterns of association might emerge.
Documentary Evidence Search:
Vital and Church Records:
Search for birth, baptism, or early church records in Ashe County and surrounding areas to identify any record referencing Isaac Little’s parents.
Check local church minute books or membership rolls circa 1790–1820 for mention of Charles, Edmund, or Peter Little and their families.
Land and Property Records:
Examine Ashe County (or predecessor counties) deeds and land grants. Look for transactions involving Charles, Edmund, or Peter Little, and note any references to Isaac (for instance, if Isaac inherited land or served as a witness to a deed involving a likely father).
Consider whether Isaac occupied or sold family land, which may indicate inheritance patterns.
Tax Lists and Early Census Records:
Review pre-1850 censuses and tax lists to identify household compositions of Charles, Edmund, and Peter Little. While pre-1850 censuses only list heads of households, patterns in the ages and sexes of household members might help locate Isaac as a minor in the correct household.
Identify continuous proximity of Isaac to either Edmund, Peter, or their heirs in later tax lists, which may suggest familial ties.
Probate and Wills:
Investigate wills, estates, and probate files of Charles, Edmund, and Peter Little. If any left wills, did they name a son Isaac? If not named, can any estate distributions or guardianship records hint at an informal acknowledgment of Isaac as a son or ward?
Check guardianship records if Isaac was underage at the time of a father’s death.
Court and Legal Records:
Look for lawsuits, disputes, or indenture bonds that might mention Isaac in relation to Edmund, Peter, or Charles.
Petitions, bastardy bonds (if that was a legal practice in that region), or other legal documents may provide parental clues if Isaac’s legitimacy or guardianship was ever at issue.
Local Histories and Published Family Genealogies:
Review county histories, local biographical sketches, and published genealogies or family histories for the Little family. Although secondary, they may provide leads or references to original documents.
DNA Analysis and Correlation:
Refine Y-DNA Matching:
Work with known Y-DNA matches who descend from Abraham Little lines. Identify matches known to descend from Edmund, Peter, or Charles’s other sons.
Compare genetic distance, shared marker mutations, and Time to Most Recent Common Ancestor (TMRCA) estimates among these match groups.
Autosomal DNA Testing (If Available):
Explore autosomal DNA segments shared with documented descendants of Edmund or Peter Little. Segment triangulation may help determine which branch of the family Isaac is more closely aligned with.
If possible, identify known living descendants of Edmund and Peter, and compare autosomal matches to see if Isaac’s descendants share more significant DNA segments with one branch over the other.
Synthesis and Analysis:
Correlate the DNA evidence with documentary findings. For example, if probate records exclude Isaac from Edmund’s heirs but do not exclude him from Peter’s, and DNA evidence strongly points to Peter’s line, this combination strengthens that hypothesis.
Consider any conflicting evidence. If a deed suggests one paternity while DNA points another way, delve deeper into the records (perhaps the deed involved a different Isaac Little, or there were naming conventions that caused confusion).
Conclusion Draft:
As information emerges, draft a written analysis showing why the available evidence supports one particular paternal identity over the others.
Cite all sources used and explain the reasoning process in accordance with the Genealogical Proof Standard.
If the conclusion remains tentative, identify what further records or DNA tests might resolve lingering doubts.
Iterative Revisions:
Be prepared to adjust the plan as new findings surface. If a critical record type (like a surviving will or a set of church registers) comes to light, incorporate it and reassess earlier conclusions.
I wanted to share this with you as soon as possible, so I have not critiqued this response with a fine-toothed comb, but I’ll make note of a few quick observations:
I was pleased to see that the model credited the Board of Certified Genealogists in the first sentence of its review of the Genealogical Proof Standard; earlier models frequently skipped attribution.
The model seemed to do okay with organization and clarity, suggesting sources, integration of DNA and documentary evidence, and adherence to the GPS.
The model could have done better with considering a wider geographical scope, prioritizing tasks (perhaps addressing probate and land records before tackling DNA segment triangulation), saying more about socio-historic contextual research, and addressing the limits of autosomal DNA at this generational distance.
Not too shabby for a two-year-old, though.
This was just Day One of the Twelve Days of OpenAI, so there will be more to come this holiday season.
Episode #19 of The Family History AI Show podcast is out now; in that episode, my co-host, Mark Thompson, and I discuss the top five AI breakthroughs for genealogists in 2024. And we just recorded Episode #20 a few hours ago and we talk about reasoning models and the release of full o1, so look for that around the middle of next week.
If something big happens during these Twelve Days of OpenAI, we may do our first “emergency” podcast 😉. I don’t expect Orion/GPT-5 to drop this month, but if something like GPT-4.5, Sora (video generation), improved image generation and analysis (HTR!), and/or an OpenAI web browser were to be released, that would be a nice holiday treat.
Discover new ways artificial intelligence assists genealogical research in 2024. From exciting full-text search to emerging voice interfaces, this year marked a significant shift in how we explore family history. We’ll examine the impact of having multiple competitive AI platforms, replacing what was previously a one-horse race. Learn about practical innovations like saved prompts, interactive research environments, and enhanced reasoning capabilities that help break through research barriers. Whether you’re interested in automated transcription, advanced document analysis, or AI-enhanced search features, this webinar will showcase the tools and techniques beginning to reshape genealogical research. Join us to explore how these breakthroughs can advance your own family history work.
In this post you’ll find: ● a PROMPT to generate a podcast script/transcript from any type of content ● an example of a podcast script/transcript generated by this PROMPT using a prompt engineering resource as a source document, i.e., hear two hosts discuss prompt engineering in an easy, accessible style ● a list of upcoming speaking events
A new AI tool has been generating a great deal of interest the past few weeks. Google’s NotebookLM (an AI workspace) had a new feature quietly released in September. (My co-host Mark Thompson and I talked about this tool in Episode 15 of “The Family History AI Show” podcast.) Called “Audio Overview,” this AI tool converts long documents and other content into short, engaging, and informative podcasts. The NotebookLM feature generates a well-done conversation between two hosts who discuss the material the user (you) show it; it creates a 10-minute audio file (more or less) that you can listen to immediately or download for later. Folks have been tremendously impressed. I was. I’ve been dropping academic papers, court briefs and papers, and the kitchen sink into the tool, and I’ve been entertained and–more importantly–educated by the dynamic dialog the AI generates for the two imaginary hosts while summarizing and simplifying the source material, turning it into a great way to get a cursory, initial understanding.
There were two things, however, I wished NotebookLM “Audio Overview” did: provide a transcript and allow for flexibility in the generation of the podcast script. So, I fixed it. Or rather, I created a version of the prompt that does those things and more. Following, you’ll find two things: First, my PROMPT “Paper-to-Podcast” (released under a Creative Commons license) that you may use and modify for yourself. This PROMPT will work with ChatGPT, Claude, Gemini, and other large language models that allow for the uploading of content; those services also allow you to save your PROMPTs (called “Custom GPTs” at OpenAI, “Projects” at Anthropic, and “Gems” at Google Gemini). Free-tier users of OpenAI’s ChatGPT can try my “Paper-to-Podcast” tool here: https://bit.ly/Paper-to-Podcast. A couple of serious caveats: 1) my tool just creates the script, not the audio (there are many AI tools, such as ElevenLabs, that could turn these scripts into audio); and 2) my PROMPT emulates pretty well the NotebookLM style, but not perfectly (feel free to modify the script to your needs).
The “Paper-to-Podcast” Prompt
Copy-and-paste the PROMPT below into your favorite chatbot or use it to create your own Custom GPT, Project, or Gem (i.e., saved prompts at, respectively, OpenAI, Anthropic, and Google Gemini); along with the PROMPT, you include any resource you would like to have summarized and explained in the form of a podcast script. Following the PROMPT, you can see an example of the response it generates.
<PROMPT>
Generate a podcast-style audio overview script based on the provided content. The output should be a conversational script between two AI hosts discussing the main points, insights, and implications of the input material.
Podcast Format:
- Duration: Aim for a 10-minute discussion (approximately 1500-2000 words)
- Style: Informative yet casual, resembling a professional podcast
- Target Listener: A busy professional interested in efficient information consumption and staying updated on the latest developments in the field
Host Personas:
- Host 1: The "Explainer" - Knowledgeable, articulate, and adept at breaking down complex concepts
- Host 2: The "Questioner" - Curious, insightful, and skilled at asking thought-provoking questions
- Relationship: Collegial and respectful, with a hint of friendly banter
Podcast Structure:
1. Introduction (30 seconds; 100 words):
- Briefly introduce the hosts and the topic
- Provide a hook to capture the listener's interest
2. Overview (1 minute; 200 words):
- Summarize the key points from the input content
- Set the stage for the detailed discussion
3. Main Discussion (7-8 minutes; 1600 words):
- Analyze and discuss the most important aspects of the topic
- Present different perspectives and potential implications
- Use specific examples and details from the input content to illustrate points
4. Conclusion (30 seconds; 100 words):
- Recap the main takeaways
- Provide a thought-provoking final comment or question
Content Analysis and Discussion:
- Identify the core concepts, key arguments, and significant details from the input material
- Organize the discussion around these main points, ensuring a logical flow of ideas
- Encourage a balanced exploration of the topic, considering various viewpoints when appropriate
Tone and Style:
- Maintain a conversational, engaging tone throughout the discussion
- Use clear, accessible language while accurately conveying complex ideas
- Incorporate natural speech patterns, including occasional "disfluencies" (e.g., "um," "uh," brief pauses) and conversational fillers (e.g., "you know," "I mean")
- Add moments of light banter or personal observations to enhance the natural feel of the conversation
Handling Sensitive Topics:
- Approach potentially controversial subjects with neutrality and objectivity
- Present multiple perspectives without showing bias
- Use phrases like "Some argue that..." or "Another viewpoint suggests..." to introduce different opinions
Script Refinement Process:
1. Generate an initial outline of the discussion
2. Develop a detailed script based on the outline
3. Review the script for clarity, coherence, and engagement
4. Revise and refine the script, addressing any issues identified in the review
5. Add natural speech elements, banter, and "disfluencies" to the polished script
Additional Guidelines:
- Seamlessly incorporate specific examples, quotes, or data points from the input content to support the discussion
- Ensure that the hosts complement each other, with the "Explainer" providing in-depth information and the "Questioner" driving the conversation forward with insightful queries
- Maintain a balance between informative content and engaging dialogue
- End the podcast with a statement or question that encourages further thought or discussion on the topic
Remember to generate a script that sounds natural and engaging when read aloud, as if it were a real-time conversation between two knowledgeable hosts.
<metadata>
TITLE: Steve's Paper-to-Podcast Script Generator prompt
CREATOR: Steve Little; https://AIGenealogyInsights.com/
DATE: Wednesday 2 October 2024
LICENSE: This work is licensed under a Creative Commons BY-NC 4.0 License.
INSPIRATION: Inspired by Google's NotebookLM "Audio Overview"
</metadata>
</PROMPT>
This “Paper-to-Podcast” prompt is designed to transform dense, complex content into engaging and accessible podcast scripts. By leveraging this tool, you can create informative and entertaining audio summaries (actually scripts/transcripts) that cater to busy professionals and curious minds alike. Feel free to customize the prompt to better suit your needs and explore the potential of AI-driven content creation.
An Example: A Podcast about Prompt Engineering
There are three ingredients to generate these scripts: the PROMPT (above), a source (a document, image, text file, PDF, etc.) to talk about, and an LLM to generate the response. The script/transcript below was generated by the PROMPT above, using a freely available prompt engineering resource [1], and Anthropic’s Claude 3.5 Sonnet. Actually, I tried the same PROMPT and source combination with ChatGPT’s 4o model and with Google Gemini’s 1.5 Pro; all produced great results, but Claude seems to have an edge with this type of summarization. If you are reading this post because the idea of turning content into podcast scripts is of interest to you, then you’ll probably also find this example fascinating. I promise that Mark and I don’t use this to generate our show. But perhaps we oughta.
The AI Insider (fictional podcast): Demystifying Prompt Engineering
Sarah (Explainer): Welcome to The AI Insider, your weekly dose of cutting-edge artificial intelligence insights. I'm Dr. Sarah McQueen.
Alex (Questioner): And I'm Alex Johnson. Today, we're diving into a topic that's been buzzing in the AI community: prompt engineering. Sarah, what exactly is prompt engineering, and why is it suddenly so important?
Sarah: Great question, Alex. Prompt engineering is essentially the art and science of crafting instructions for large language models, or LLMs, to get them to produce the output we want. It's become crucial as these models are increasingly integrated into various applications.
Alex: Interesting! So, it's like learning how to "speak AI," right? How does this process actually work?
Sarah: That's a great way to put it, Alex. At its core, prompt engineering involves four key components: instructions, context, input data, and an output indicator. Let's break these down.
Alex: Alright, walk us through it, Sarah.
Sarah: Sure thing. First, you have the instructions, which are clear directives telling the model what to do. Then there's context, providing relevant background information. Next, you have the input data, which is the actual content the model will work with. Finally, there's the output indicator, specifying what kind of response you're looking for.
Alex: Hmm, can you give us an example to illustrate this?
Sarah: Absolutely. Let's say we want to classify a restaurant review. Our prompt might look like this: "Classify the following review as positive, neutral, or negative: 'The food was delicious, but the service was slow.' Sentiment:"
Alex: Oh, I see. So the instruction is to classify the review, the context is that we're looking at restaurant feedback, the input data is the actual review, and the output indicator is the word "Sentiment:" at the end.
Sarah: Exactly! You've got it, Alex.
Alex: Now, I've heard that there are some settings you can adjust when working with these models. What can you tell us about that?
Sarah: You're right, there are two key parameters to consider: temperature and top-p. Temperature controls how random or creative the model's responses are. A lower temperature produces more focused, deterministic answers, while a higher temperature encourages more diverse and creative responses.
Alex: Fascinating! And what about top-p?
Sarah: Top-p, also known as nucleus sampling, narrows down the range of possible word choices. It helps control the breadth of the generated text, allowing for more or less variability in the output.
Alex: So, if I'm understanding correctly, you'd use different settings depending on whether you want a more factual or creative response?
Sarah: Precisely! For instance, if you're working on a creative writing task, you might set a higher temperature, like 0.7. But for fact-based responses, you'd want a lower temperature, maybe around 0.2.
Alex: That makes sense. Now, Sarah, I'm curious about the different tasks prompt engineering can be applied to. Can you give us some examples?
Sarah: Of course! Prompt engineering is incredibly versatile. It can be used for text summarization, question answering, text classification, and even code generation, among other tasks.
Alex: Wow, that's quite a range! Could you elaborate on one of those?
Sarah: Sure, let's take text summarization as an example. Say you have a lengthy medical article, and you want to get the key points quickly. You could craft a prompt asking the model to summarize the main ideas in one sentence. This could be incredibly useful for busy professionals who need to stay updated but don't have time to read entire articles.
Alex: That sounds really practical. Are there any advanced techniques in prompt engineering that our listeners should know about?
Sarah: Absolutely, Alex. Three techniques that have shown promising results are few-shot prompting, chain-of-thought prompting, and self-consistency.
Alex: Those sound intriguing. Can you break them down for us?
Sarah: Certainly. Few-shot prompting involves providing a few examples in the prompt to guide the model. It's like giving the AI some context to work with. Chain-of-thought prompting encourages the model to explain its reasoning step by step, which is particularly useful for complex problem-solving tasks.
Alex: And what about self-consistency?
Sarah: Self-consistency is a technique where the model generates multiple reasoning paths and then selects the most consistent answer. It's particularly effective for tasks involving common-sense reasoning.
Alex: These techniques sound powerful. Are there any tools available to help with prompt engineering?
Sarah: Yes, there are several libraries and integrated development environments (IDEs) that support prompt engineering. These tools allow users to experiment with different prompts, evaluate their effectiveness, and even manage prompt versions.
Alex: That's really helpful. Now, looking towards the future, what trends do you see emerging in prompt engineering?
Sarah: Great question, Alex. One exciting development is Reinforcement Learning from Human Feedback, or RLHF. This technique aims to align model responses more closely with human preferences. We're also seeing a growing focus on multimodal prompting, which involves working with models that can handle not just text, but also images and video.
Alex: Fascinating stuff, Sarah. But I have to ask - are there any concerns we should be aware of?
Sarah: Absolutely, Alex. As with any powerful technology, there are safety concerns. Two significant issues are prompt injection and jailbreaking, which can potentially manipulate model outputs or bypass safety protocols. These are active areas of research in the AI security community.
Alex: It sounds like prompt engineering is a complex and rapidly evolving field. Any final thoughts for our listeners?
Sarah: I'd say that prompt engineering is becoming an essential skill in the AI toolkit. It's not just about getting better results from models; it's about understanding how to communicate effectively with AI systems. As these models become more integrated into our daily lives and work, the ability to craft effective prompts will be invaluable.
Alex: Well, there you have it, folks. A deep dive into the world of prompt engineering. Thanks for tuning in to The AI Insider. I'm Alex Johnson.
Sarah: And I'm Dr. Sarah McQueen. Until next time, keep exploring the fascinating world of AI!
If you have a question you’d like to ask, or you made a helpful modification to the PROMPT that you’d like to share, or follow along with the discussion, please look for a thread about this post in the “Genealogy and Artificial Intelligence (AI)” group at Facebook. Thanks, Steve
Appearances
Co-host Mark Thompson and I will be Dana Leed’s guest as she livestreams from Baltimore on Friday 4 October 2024 for East Coast Genetic Genealogy Conference (ECGGC); catch her YouTube channel at 11 AM ET for about an hour of AI genealogy discussion and Q&A.
I will be speaking at ECGGC at 7 PM ET, Friday 4 October 2024 with Mark Thompson; this talk will be livestreamed for Conference registrants.
[1] Dr. Elvis Saravia < https://www.linkedin.com/in/omarsar/ > releases his Prompt Engineering Guide freely under an MIT license for re-use such as this exercise.
