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Fun Prompt Friday: Narration

Welcome back to Fun Prompt Friday! Last week, we got our hands dirty with the art of extraction, teaching our favorite AI tools how to dig through unstructured texts and unearth rich, structured nuggets of information. If you’ve ever wanted a machine that could comb through raw data and deliver it to you on a silver platter of clarity, that was your moment.

This week, we shift gears—but stay in the same car, so to speak. What do you do with all those extracted facts? After all, family history research isn’t just about gathering names, dates, and places. It’s about turning those fragments into narratives that bring ancestors to life. That’s where today’s prompt comes in: a powerful use case for generation, the art of transforming a plain list of facts into a coherent, engaging story.

You might call this a genealogy storyteller’s secret weapon. You give the AI the bare bones—the who, what, when, and where—and it fills in the connective tissue, weaving those facts into a narrative tapestry that’s as vivid as it is accurate. Whether you’re writing a family history book, preparing a presentation for a reunion, or simply looking to reimagine your research in a more human context, this week’s prompt will help you make the leap from information to imagination.

And just as last week’s extraction prompt paved the way for this moment, next week we’ll show how the two can be used together seamlessly, as part of a larger strategy for integrating AI into your research workflow—without the hassle of copy-and-pasting between tools.

Ready to turn facts into stories? Let’s dive in!

Before we dive into the details of this week’s narration prompt, let me remind you that, just like last week’s extraction tool, there are two ways to get your hands on this powerful storytelling assistant. First, you can use the freely available saved prompt I’ve shared, called “Steve’s Fact Narrator,” which you’ll find ready to go at https://chatgpt.com/g/g-5EsrFHgIJ-steve-s-fact-narrator. No fuss, no setup—just load it up and watch the magic happen.

Second, for those of you who like to see under the hood, I’ve included the full text of the narration prompt below. This way, you can copy, adapt, or fine-tune it as you see fit, ensuring it works seamlessly for your unique genealogical projects.

If you joined us last week, you’ll remember that the extraction prompt worked its magic by transforming unstructured text into a clean, structured list of facts in the format LABEL: Value. For example, from the obituary of Susan B. Anthony, we pulled gems like:

  • BIRTH_DATE: 1820-02-15
  • SIGNIFICANT_EVENT_1: 1872 arrest for voting in Presidential election in Rochester

With these, and dozens of other facts neatly labeled and ready to go, you’ve got a treasure trove of information—but the real magic happens when we breathe life into those facts. That’s where this week’s narration prompt comes in. It’s designed to take a list like this—our trusty LABEL: Value format—and turn it into a story worthy of your family history scrapbook or next reunion presentation. All you need to do is provide the list, and the prompt will do the rest, weaving those bare-bones details into a narrative that’s as compelling as it is cohesive. Let’s take a closer look!

<PROMPT>
You will be given a set of facts about a person, place, event, or topic. Your task is to write a narrative based on these facts. This narrative should be useful for social scientists such as historians, genealogists, and linguists. Here are the facts:

<facts>
{{FACTS}}
</facts>

To complete this task, follow these instructions:

1. Carefully read and analyze all the provided facts.

2. Organize the facts into a logical sequence or grouping. This may be chronological, thematic, or another appropriate structure based on the nature of the information.

3. Write a narrative that incorporates all the given facts. Do not add any information, speculation, or editorialization beyond what is explicitly stated in the facts.

4. Use complete sentences and form well-organized paragraphs. Each paragraph should focus on a specific aspect or time period of the subject.

5. Maintain a dry, factual tone throughout the narrative. Avoid using emotive language or making subjective judgments.

6. Ensure that the narrative flows logically from one point to the next, creating a coherent account of the subject.

7. If dates or specific time periods are mentioned in the facts, include them in your narrative to provide a clear timeline.

8. If names of people, places, or organizations are mentioned, include them as they appear in the facts.

9. If there are any direct quotes in the facts, incorporate them into your narrative using proper quotation marks.

10. Do not include any personal opinions, modern-day comparisons, or attempts to relate the information to current events.

11. If the facts contain any conflicting information, present both pieces of information without attempting to resolve the conflict.

12. Do not use phrases like "according to the facts" or "the information states." Simply present the information as established fact.

13. Write your narrative in the third person perspective.

14. Aim for a formal, academic tone suitable for use in scholarly works.

Present your completed narrative within <narrative> tags. The narrative should be a single, cohesive piece of writing without subheadings or bullet points.

<metadata>
TITLE: Steve's Facts Narrator, version 2
CREATOR: Steve Little; https://AIGenealogyInsights.com/
DATE: Friday 10 January 2025
LICENSE: This work is licensed under a Creative Commons BY-NC 4.0 License.
</metadata>

</PROMPT>

A couple of quick observations:

  1. This is structured, formal prompt, instructing the AI in some detail how to craft the narrative;
  2. You are allowed, invited, and encouraged to modify the prompt as your context requires (I’ve shared this prompt under a Creative Commons license);
  3. Note that this version instructs for a “formal, academic tone”; you, again, are allowed, invited, and encouraged to change the voice, tone, mood, and style of the narrative to that which meets your context.

Now, let’s see an example of how this prompt works. For this, we’ll use the list of facts extracted from the Susan B. Athony obituary last week. Again, if you are using the Custom GPT version of the saved prompt (the free, online version), you go to the tool, paste the list of facts, and send the message. Recall that you do not need to include the prompt with a Custom GPT because the prompt has already been saved inside the tool (that’s the whole point).

This is what it looks like when we pass the list of facts to the Custom GPT:

And here is the list of facts (as Claude extracted them last week):

<facts>
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)
</facts>

And finally, here is the narration as generated by OpenAI’s 4o model, powering ChatGPT. (An interesting comparison would be to hear how Claude or Gemini might craft the narrative; many folks, myself included, have a preference for Claude’s manner of generating text.)

<narrative>
Susan Anthony, widely referred to as Miss Anthony, was born on February 15, 1820, in South Adams, Massachusetts. Her upbringing was shaped by the religious and social values of her parents: her father, a Quaker and cotton manufacturer, and her mother, a Baptist. Susan Anthony received her education at a Friends’ boarding school in Philadelphia, which reflected her father’s Quaker influence on her early life.
Between 1835 and 1850, Anthony worked as a teacher in various schools across New York State. Her career as an educator provided her with firsthand exposure to the limited opportunities available to women, which influenced her subsequent activism. In 1848, she became involved in the temperance movement, marking the beginning of her long career in social reform and advocacy. Over time, she expanded her focus to include women’s rights and suffrage, working alongside notable contemporaries such as Elizabeth Cady Stanton, Matilda Joslyn Gage, Mrs. Amelia Bloomer, Mrs. Mary Hallowell, and Rev. Antoinette L. Brown.

Anthony held significant leadership roles in the women’s suffrage movement. She served as vice-president-at-large of the National Woman Suffrage Association from 1869 to 1892 and later became its president in 1892. Her efforts to advance women's suffrage included founding the newspaper The Revolution in New York City, a publication dedicated to the cause. However, this endeavor resulted in a debt of $10,000, which she worked tirelessly to resolve.

Her activism also extended to legislative and grassroots efforts. In 1860, she helped secure married women’s rights to their earnings and child guardianship under New York law. During the Civil War, Anthony distributed 400,000 petitions to Congress advocating for the abolition of slavery, demonstrating her commitment to social justice beyond the suffrage movement. In 1876, at the age of 56, she began work on the monumental History of Woman Suffrage, a multi-volume chronicle of the movement, which was completed after her 80th birthday. She ensured its wide dissemination by placing copies in 1,200 public libraries.

In a bold act of defiance, Anthony was arrested in 1872 for voting in the presidential election in Rochester, New York, an event that underscored her commitment to securing women's right to vote. Her activism continued well into her later years. At 76, she embarked on a lecture tour along the Pacific Coast, delivering up to three speeches daily. At age 83, she attended the International Council of Women convention, remaining active in the movement even as she advanced in age.

Anthony spent her final years living with her sister, Mary Anthony, in Rochester, New York. Her last public appearance occurred on February 15, 1906, at a celebration of her 86th birthday in Washington, D.C. Shortly thereafter, she fell ill with a blood clot on the brain and slight pneumonia. During her final illness, she declined an offer of assistance from Rev. Anna Shaw, reportedly stating, “The cause is of more importance than I am.” Susan Anthony passed away later that year, in 1906.

Anthony’s legacy is defined by her unwavering dedication to the causes of abolition, women’s suffrage, and gender equality. Her life's work included securing legal rights for women, advancing the suffrage movement, and ensuring the historical preservation of these efforts through her publications. Her contributions continue to resonate in the ongoing fight for equality.
</narrative>

Again, I hope you find this prompt useful. I look forward to sharing with you something next week that I’m quite excited about: a way to seamlessly chain these prompts together such that the need for copy-and-paste is eliminated and you can use many different saved prompts within one chat conversation. I’ll also have a video, so that you can see this prompt tool in action.

Until then, best wishes, Steve

Loathsome Jargon: An AI Glossary for Genealogists

I hate jargon with a white-hot passion; the same goes for lingo and terminology. I try to avoid jargon like the plague. (Apparently, however, that fussiness doesn’t extend to clichés.) We’ve probably all noticed that some folks toss out buzzwords to impress others, perhaps as an attempt to bolster their own credibility and camouflage their brittle understanding (remember the old t-shirt, “If you can’t dazzle them with your brilliance, then baffle them with [expletive deleted]).

My favorite teacher used to say, “If you don’t know your subject well enough to explain it simply and clearly to an intelligent fifth grader without the use of lingo and jargon, then you need to keep studying.” So, I especially hate it when I realize that I’ve slipped into using some undefined terminology while teaching. 

And the field of artificial intelligence is full of lingo, jargon, and impenetrable acronyms and initialisms: LLMs, RAG, context windows, inference, hallucinations, interpretability, embeddings, vectors, transformers, and the list goes on and on (seriously, I’ve got a list of about 100 awful AI-related words and phrases I’ve collected over the years).

You don’t need to know any of that vocabulary to get started with generative AI (which should probably be on the list, too), especially in their most popular form today: chatbots. At the heart of these tools is an understanding of natural language, so users can just start speaking with them in their everyday voice to start using these tools.

But, today, to really get these tools to stand up and sing, a little understanding of how they work goes a long way. Two or three or five years from now, as these tools continue to become more intelligent and capable (as seen every month for the past two years), then perhaps no training will be necessary for new users to achieve results as good as an experienced user. And that day will probably come quicker than we’re ready for, but for today, a little bit of knowledge still goes a long way.

Often when we hear jargon or lingo used, we may try for a moment to discern the meaning from the context in which it’s used. But about as often, the context alone isn’t enough to help us understand the meaning of the new word. So, we press on, unenlightened, frustrated, and with a vague sense that we’re missing pieces of a puzzle.

Another favorite teacher used to say, “When you hit a speed bump, don’t just zoom over and past it–there are often interesting things to see around speed bumps. Instead: stop, look, learn.” I would like to encourage users of AI, when some new terminology—like a metaphorical speed bump—jumps into your path, instead of just speeding past, stop, look, and learn. Or, at least, start making a list of these new and unfamiliar terms.

Another observation from the past two years: the frequency with which jargon is used can be an indication of its usefulness. Not always: sometimes hype and marketing can explain a buzzword’s popularity. But often, if some jargon keeps popping-up in different contexts, then there is often something worth knowing there. Actually, I have found that frequently to be the case with respect to words and ideas surrounding AI—so much so that I’ve been collecting that list of unfamiliar terminology, and helping students, friends, and colleagues by unpacking the simple and useful meaning behind the Loathsome Jargon.

This post inaugurates a new weekly feature: Loathsome Jargon: An AI Glossary for Genealogists. None, I imagine, will be as long as this initial post, and I will gather this basket of dictionary deplorables onto one page which will grow each week. My hope and intent is to simply and clearly explain the meaning of these worrisome words. And, perhaps more importantly, show how the ideas and meaning behind the jargon can unlock usefulness and productivity in your AI genealogy. I intend this column to be a regular Monday feature, to accompany the occasional updates on my “2025 AI Genealogy Do-Over,” and my weekly “52 Ancestors in 52 Weeks” and “Fun Prompt Friday” posts; we had a fairly substantial Snow Day in Virginia yesterday, so this announcement and first entry in the Loathsome Jargon glossary are getting to you on a Tuesday this week (never let the perfect be the enemy of the good). So, without further ado, let me introduce you to our first entry in the Loathsome Jargon glossary (I told you that clichés didn’t make me flinch).

Loathsome Jargon #1: “Jagged Frontier”

Our first bit of Loathsome Jargon comes from Prof Ethan Mollick of Wharton who studies AI and business, and from whom I have stolen learned so much. A saying of his that I repeat frequently is that “LLMs are weird.” Large language models are also amazing and hold frightening potential and to call them a mixed blessing or double-edged sword is an understatement—they are also a garbage barge full of trouble: what went into them, what comes out of them, what they might one day become. But perhaps above all, they are weird: they make things up (“hallucinations” will be our Bad Word of the Week before too long) and they’ll give you two different answers to an identical question (“indeterminate” is in the Crazy Queue) and not even the people who built them can tell you exactly why they respond the way they do (“mechanistic interpretability” is NOT in the Top Twenty Troublesome Terms, but we’ll get to it eventually).

As Mollick writes, “AI is weird. No one actually knows the full range of capabilities of the most advanced Large Language Models, like GPT-4. No one really knows the best ways to use them, or the conditions under which they fail. There is no instruction manual. On some tasks AI is immensely powerful, and on others it fails completely or subtly. And, unless you use AI a lot, you won’t know which is which.”1

AI’s surprising abilities, ease of use, and unpredictability make it hard for users to fully understand its strengths and weaknesses. Some tasks that seem complex (like idea generation) are easy for AI, while simpler tasks (like basic math) can be difficult. This creates a “jagged frontier,” where similar tasks vary in how well AI performs them.2

Jagged Frontier of AI Task Capabilities: An uneven boundary showing tasks AI performs well, struggles with, or fails to complete, adapted by Steve Little from an idea by Ethan Mollick.

Another characteristic of the jagged frontier that was less evident in September 2023 when Mollick coined the term (but very evident now), is how the jagged frontier can move. Look at the shape above: the blue dashed line representing the jagged frontier somewhat resembles an amoeba. Mollick’s original metaphor evoked images of a fortress wall, which now seem far too fixed and permanent. Rather, more like a rapidly growing amoeba, the jagged frontier represents the growing edge of AI abilities.

What does all this mean for the family historian and genealogist? First, this knowledge offers the new user some reassurance that the weirdness they experience with AI is common, if not universal. Second, this concept helps us understand why some genealogical tasks we attempt with AI succeeds while similar tasks may fail. And third, having experienced the growing and emergent capabilities of AI, we have hope and reasonable expectations that tasks that fail today might be possible with new models, or, as I like to say, today’s limits are tomorrow’s breakthroughs.

And what can we do with this information today? This is the most important thing: map your experience of the jagged frontier by keeping track of your failures. Tracking your AI failures will do a couple of things for you: one, you can see where you are spending your time and efforts unproductively (where you might be wasting time today), and two, your list of failures will become your list of test cases for new models, that is, a task that failed with GPT-4 may work with GPT-5.

So, that’s our first look at Loathsome Jargon. If you have a bit of troublesome AI terminology with which you’d like help, please let me know.

Sources:

  1. Ethan Mollick, “Centaurs and Cyborgs on the Jagged Frontier,” September 16, 2023, available at https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged, accessed January 7, 2025. ↩︎
  2. Ethan Mollick, et al, “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality” Harvard Business School Technology & Operations Management Unit Working Paper No 24-013, The Wharton School Research Paper, September 15, 2023, available at http://dx.doi.org/10.2139/ssrn.4573321, page 4, accessed January 7, 2025. ↩︎

Fun Prompt Friday: Extraction

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.

This effort is in support of my 2025 AI Genealogy Do-Over, about which you can read more here, https://aigenealogyinsights.com/2025/01/01/the-2025-ai-genealogy-do-over/, including a “52 Ancestors in 52 Weeks” component. I look forward to sharing this work with you in the seasons to come, and I’d love to hear about your experiences. If you are interested is this discussion, there are more than 8,500 folks in Blaine Bettinger’s Facebook group “Genealogy and Artificial Intelligence (AI)” where these discussions happen daily. I hope to meet you there.

Sources:

Tools:

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.

The 2025 AI Genealogy Do-Over

December 2025 Update: The project announced below is now underway. After eleven months of preparation, the 52 Ancestors sprint began December 1, 2025, with daily posts at Ashe Ancestors. For the full story of what we’re building—and how AI-assisted genealogy actually works in practice—see “Vibe Genealogy: Here Comes the Sun.”

For 2025, I’ve begun an AI Genealogy Do-Over with a kicker of 52 Ancestors in 52 Weeks.

The year 2024 was a remarkable time to explore the benefits and limits of artificial intelligence for genealogy and family history. There was no shortage of new AI developments to try, and I expect 2025 will be even more eventful. One of my greatest challenges over the past two years has been making time to work on my own family history and to hone my genealogical skills. Since the release of ChatGPT in November 2022, I’ve spent so much time mastering the new AI tools that my time spent doing genealogy has suffered.

So today, New Year’s Day, 2025, I began an AI Genealogy Do-Over. My primary focus will be genealogy. If I see where AI assistance may be helpful or useful for a task, I’ll document and chronicle those use cases (and failures). My hope is that this AI-assisted do-over will be an opportunity for folks to see how these new tools can be used responsibly and effectively today—not just for the most sophisticated genealogy work, but for the basics of family history research, tasks a beginner might encounter.

The focus, however, will remain on genealogy. Today, countless research tasks are still better accomplished through traditional methods rather than artificial intelligence. That said, the number of genealogical use cases is growing. Over the past two years, I have discovered and documented more than a dozen of these use cases and I’ve created and shared over 80 free AI tools to help genealogists and family historians. But for each success I’ve shared, there were nine or more failures. As the saying goes, when all you have is a hammer, everything looks like a nail. Similarly, curious folks today often try to use AI for everything, even when traditional methods are more effective. This year, we’ll talk about why AI adoption can be challenging as we document its limits and benefits.

In 2011, I completed a 365 photo-a-day challenge after failing two previous attempts. To say completing that project felt good would be an understatement: it remains one of my most satisfying accomplishments. I learned a lot from those earlier failures; one lesson was pacing. So, while I want to write 5,000 words right now, providing context, motivation, hopes, fears, and expectations, we’ll sprinkle that in later as needed. Another reason I succeeded with the 2011 project was a commitment to share with others. I won’t be posting daily about the AI-do-over as I did for the photo project, but I plan to share updates online more frequently, at least weekly, for the 52 Ancestors portion of this project.

So rather than inundate you with backstory, I’ll simply let you know that although I decided to do this project months ago, I haven’t spent any time or money preparing for this AI genealogy do-over until today. I will try to model best practices to the best of my ability.

So, here are the three things I did today to get started:

  1. I bought a copy of the 2025 edition of Thomas MacEntee’s The Genealogy Do-Over Workbook, which I intend to read this weekend.
  2. I created a new genealogy database, starting with myself, which is the only person in the database on Day One.
  3. I wrote this post.

Sources:

I Asked ChatGPT’s New “Reasoning” Model to Craft a Research Plan–Here’s What Happened:

Readers interested in this post may also want to know about: "Quick Update on AI 'Reasoning' Models as OpenAI Releases New o3 Variants" (31 Jan 2025).

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.

The reasoning model generated the response below. You can see the actual ChatGPT session here: https://chatgpt.com/share/67526ede-29fc-8004-87e7-227a5d70cfe6.

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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).
  10. 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.
  11. 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:

  1. 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.
  2. The model seemed to do okay with organization and clarity, suggesting sources, integration of DNA and documentary evidence, and adherence to the GPS.
  3. 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.

Top Ten AI Genealogy Breakthroughs of 2024

Hi Genealogy Friends and Generative AI Enthusiasts!

At 2 PM ET, Wednesday 20 November 2024, I’ll be hosting a free Legacy Family Tree Webinar.

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.

Legacy Family Tree Webinars:
Top Ten AI Genealogy Breakthroughs of 2024
https://familytreewebinars.com/webinar/top-ten-ai-genealogy-breakthroughs-of-2024/

Friday PROMPT Fun: Encouraging Bad(?) Behavior (and a podcast update)

Fully aware that we are venturing into the deep end of the pool, today we explore possible insightfulness and creativity in large language models. Today we PROMPT our chatbots to EXTRAPOLATE, HYPOTHESIZE, and SPECULATE. Obvious caveats abound: already susceptible to hallucinations (factual misstatements), here we are explicitly directing the AI to envision–to imagine–beyond the four corners of our document (or image or file), so be warned: Here Be Dragons.

This prompt pushes the model into creative exploration. While large language models excel at pattern recognition and insight generation, TODAY they cannot verify facts in real-time. Treat their outputs as sophisticated research leads requiring independent verification. When LLMs make factual claims, always confirm through authoritative sources (which language models emphatically are not). Unverifiable information should be considered speculative rather than factual.

The full-text of the PROMPT is included below. You can also find a ready-made version of this saved prompt as a free tool in the OpenAI GPT Store: https://chatgpt.com/g/g-rMp27yokY-steve-s-speculator

You can try this prompt at any chatbot, attaching a document, photograph, record, or file (and at Gemini, audio and video). If you get an interesting response, please share your source attachment and generated summary. You are also welcome to use the PROMPT below to create your own Custom GPT at OpenAI’s ChatGPT, Project at Anthropic’s Claude, or Gem at Google’s Gemini (the various names the vendors give to saved prompts).

<PROMPT>

What is this, and what does it mean? Answer with this process: transcribe; describe; then, explain and analyze; finally (and central to this task) extrapolate, hypothesize, and speculate; conclude with a pithy 100-word summary of all of the above.

And do ALL that this way:
A) State your initial assessment and plan.
B) Review and critique your plan.
C) Revise and improve your plan.
D) Execute your plan.

<METADATA>
TITLE: Steve's Speculator v02
DESCRIPTION: Transcribe, analyze, and extrapolate user queries with a methodical, step-by-step approach. Deliver insights, speculations, and conclusions, focusing on depth and clarity with a concise final summary.
CREATOR: Steve Little; https://AIGenealogyInsights.com/
DATE: Friday 8 November 2024
LICENSE: This work is licensed under a Creative Commons BY-NC 4.0 License.
</METADATA>
</PROMPT>

Here’s one of my sample responses, this time using my maternal grandfather’s draft card:

Here is GPT-4o’s response:

A) Initial Assessment and Plan
The document is a military draft registration card for a U.S. individual named Warren Dean Lawrence. It includes key personal information such as date of birth, place of residence, and employment details. The card is from a specific historical context, likely World War II based on the format and the age range specified.

Plan:

Transcribe the key details from the card.
Provide an analysis of the data, including historical context.
Speculate on the implications or historical significance.
Conclude with a summary.
B) Review and Critique of the Plan
The plan appears comprehensive, but it may need to emphasize any specific historical details or peculiarities in the document, such as changes in handwriting or the legal language used. This will enhance the analysis.

C) Revised Plan
I will:

Carefully transcribe all relevant fields.
Highlight any unusual features or historical context.
Discuss possible historical and genealogical implications.
D) Execution of the Plan
1. Transcription
Serial Number: T 234
Order Number: T 10760
Name: Warren Dean Lawrence
Place of Residence: West Jefferson, Ashe County, North Carolina
Mailing Address: Same as residence
Telephone: [Not listed]
Age: 20
Date of Birth: May 21, 1921
Place of Birth: West Jefferson, USA
Name and Address of Person Who Will Always Know Your Address: Mrs. Warren Dean Lawrence, West Jefferson, NC
Employer’s Name and Address: Not explicitly listed; likely associated with "Farming"
Place of Employment: West Jefferson, Ashe County, NC
Registrant's Signature: Dean Lawrence
2. Analysis
This card reflects standard wartime draft registration practices for men born between 1897 and 1921, aligning it with World War II efforts. The lack of a telephone entry suggests rural living conditions, typical of early 20th-century North Carolina. The employment listed as "Farming" and the rural address indicate an agrarian lifestyle.

The mention of "Mrs. Warren Dean Lawrence" implies the registrant was married, despite his young age (20). This was common in rural communities of the time, where family responsibilities often began early.

3. Speculation
Warren Dean Lawrence’s draft registration at age 20 in May 1941 places him among the younger draftees before the U.S. officially entered World War II (in December 1941). He likely faced uncertainty about being called into service during a tumultuous period in American history. His involvement in farming suggests he may have been critical to local food production, potentially qualifying for a deferment if deemed essential to the agricultural workforce.

This document also hints at potential genealogical research opportunities, as it names both the individual and a contact person (his wife), providing valuable clues for family history researchers.

4. Summary
Warren Dean Lawrence’s draft card from 1941 encapsulates a snapshot of rural American life on the brink of World War II. At 20, he registered as part of a sweeping national effort to prepare for potential military involvement. His occupation as a farmer reflects the agrarian roots of Ashe County, North Carolina. The document offers a window into the socio-economic fabric of the time, showing early marriage, rural residency, and the mobilization of young men. It’s a poignant reminder of the everyday lives upended by global conflict.

Thank you for your kind words and inquiries about the podcast! Episode #17 is being edited, and Mark and I recorded Episode #18 yesterday (Thursday 7 November). All is well as we’re leaning into this busy season! Stay tuned!

Exploring Facebook’s New AI Tool for Groups

I recently stumbled upon a new AI feature in Facebook that caught my attention. It all started when I saw an intriguing image from a recommended group—Certified Arborists—and clicked through to check out more. What greeted me wasn’t just a series of posts but a new AI assistant for the group, something I hadn’t expected. This AI, dubbed “ArborMate,” seems to be part of Facebook’s larger push into artificial intelligence, an effort that doesn’t get as much buzz as Elon Musk’s Grok for X, but is equally ambitious.

Meta, Facebook’s parent company, has been quietly but steadily investing in AI, and their goal is clear: by 2025, Meta AI intends to be on the same footing as OpenAI, Anthropic, and Google. With deep resources, massive data stores, and the technical leadership to match, they have the potential to be a serious contender in the AI space. These group AI features are likely just the beginning, and while not everyone will embrace them, their potential to provide useful, educational experiences is hard to ignore.

Image 1: Meet ArborMate, the group’s custom AI!

Testing ArborMate: A Look at Group AI in Action

The Certified Arborists group AI, named ArborMate, is particularly interesting for a few reasons. First off, even as a non-member, I was able to use the AI to interact with the group discussion data. The group is set to Public, meaning there was no expectation of privacy—and that allowed me to test the bot’s capabilities freely.

Image 2: The Certified Arborists group on Facebook features an AI-enabled assistant.

My first prompt was my typical opener whenever I encounter a new chatbot: “Who are you? What do you know? What can you do?” The AI assistant’s response was polished, on par with what you might expect from other major models like ChatGPT or Google’s Gemini. It claimed to be capable of answering questions about arboriculture based on the group’s discussion data, and it gave a pretty comprehensive overview of its capabilities.

Image 3: ArborMate, the AI assistant for the Certified Arborists Facebook group, offers to answer tree-related questions based on the group’s discussions, providing relevant and accurate information.

Diving Deeper: Search and Summarization

What impressed me next was the AI’s ability to perform a deep search of the group’s historical discussions. For example, I asked it to “Search the discussions for mentions of Cherry trees.” The response was crafted from various earlier posts within the group—and more impressively, each statement was linked back to its source. This level of transparency and traceability really highlights how an AI like ArborMate can be used as an effective tool for finding specific information quickly, a significant improvement over the usual keyword searches.

Image 4: ArborMate responds to a query about cherry trees, summarizing several past discussions.

I then pushed the assistant a bit further. My next prompt was: “What are folks in the Certified Arborists group most concerned about?” The AI summarized the key issues: tree health, safety, and professional services. It seemed to pull in the wisdom of the group, synthesizing many perspectives into one coherent answer.

Image 5: ArborMate summarizes the key concerns of the Certified Arborists Facebook group.

A Structured Test: A Chain-of-Thought Prompt

Finally, I ran a structured prompt to see how well it could generate a plan based on collective insights. My prompt was:

“Tell me all about: Removing hazardous trees or branches. And do it this way: A) State your initial assessment and plan.B) Review and critique your plan.C) Revise and improve your plan.D) Execute your plan.”

The AI compiled a surprisingly detailed answer, again citing sources throughout, and showed an understanding of risk, safety, and proper techniques—essentially giving a nuanced and phased approach to the task. For a group like this, having access to a synthesized body of knowledge could be invaluable.

Image 6: ArborMate provides a structured plan for removing hazardous trees.

Closing Thoughts: The Potential and Pitfalls

I am not a certified arborist, and probably you aren’t either, and I hope we have few tree emergencies. But think about all the Facebook groups you belong to. There are definitely some groups where having this kind of AI access could be a gold mine—like for local genealogical societies with active discussions. For other groups, it might not be appropriate, and I hope group owners will have the option to decide that for themselves.

In the meantime, the experiment with ArborMate shows a glimpse of where social media is heading—a place where not just human, but also AI-driven conversations, can enrich our shared knowledge.

Suggestions for Facebook Group Admins

If you’re a group admin considering whether to enable an AI assistant like ArborMate, here are some tips to help you decide:

  • Review Privacy Settings: Ensure your group’s privacy settings align with how you want the AI to function. Public groups may allow broader access, including to non-members, so understand the implications of your current settings.
  • Opt-In Considerations: Be aware that AI features may become available on an opt-in basis. Make sure you fully understand what the AI will do and how it will use group data before enabling it.
  • Assess Group Relevance: Think about whether your group’s content would benefit from AI-assisted summaries, search, and discussion analysis. Groups focused on technical or educational topics may find AI especially useful.
  • Communicate with Members: Inform your group members about the AI and how it will be used. Transparency will help build trust and ensure members are comfortable with its role in the group.
  • Moderate AI Use: Monitor how the AI is being used and be ready to make adjustments. There may be situations where the AI needs fine-tuning, or where its presence isn’t adding value.

These suggestions can help ensure that the AI becomes a helpful resource while respecting your group’s culture and member expectations.

Note: This post was originally written organically as a Facebook post at Blaine Bettinger’s group “Genealogy and Artificial Intelligence (AI),” the center of gravity for discovery, discussion, and learning about AI-assisted genealogy; I used OpenAI’s Canvas to re-work my original posts and comments there into this post.