In part one, we explain the basics of artificial intelligence and how you might begin to think about using AI in your genealogy research.
Here, in part two, we'll go more in depth with AI expert Steve Little to help you get a bigger picture about how AI could be useful in genealogy research now and in the future. Steve is a co-host of The Family History AI Show podcast, and AI educator with the National Genealogical Society (NGS).
LTG: What is prompt engineering?
Steve:
Prompt engineering is just a fancy term for “chatting with your chat bot,” that is, giving a language model the context to respond with the next right word. By context, I like the analogy of short-term memory. The technical jargon the experts use is “context window”. During the summer and fall of 2023, ChatGPT had a context window of about three pages, or a useful over-simplification would be to say, it had the short-term memory to process about three pages. It's significantly bigger now and every month it gets bigger.
For example, when you lead the chatbot through a discussion of second cousins once removed and the formation of a GEDCOM file, you are setting the context. That is, you are putting words (and therefore ideas) about cousins and GEDCOM files in the model’s short-term memory, so to speak, so that the chatbot will have the words available to draw from and continue the discussion.
When a user and a chatbot are talking about genealogy and second cousins and GEDCOM files and parents, grandparents, great-grandparents, then all the other statistically associated words in the model’s neural network are on standby, so to speak. That is, those genealogy related words are available to be selected as a probable next right word. When you lead a LLM through a discussion, you are shaping the context of the conversation. That's what prompt engineering is now: shaping a discussion, a dialog, in a useful way.
In a sense, there are eight billion people on earth who know how to use these machines because by the time you are four years old, you know how to talk, and that's all that's required to use a language model. By the time you're four years, you know how to talk, and these tools just talk and listen.
But all human communication is fraught with the possibility of misunderstanding. The reality is that people misunderstand each other all the time because words are slippery fish. If I say the word bank, for some people, a bank is where I put my money. But if you live in Wyoming and you enjoy fishing, you stand on the stream bank or the riverbank while you fish.
If you're a pilot, how does a pilot steer an airplane? They bank the aircraft. The word bank is slippery – all words are slippery fish – and these tools are very good with slippery fish. But not perfect.
What is different and difficult for us today is that we are not accustomed to imperfect machines, not in the strange ways these tools are imperfect. Because language models may not always understand the context of our conversations, it may surprise us. We think of these algorithms as imperfect, and we're not used to that. It may seem usefully creative in one context and factually challenged at the next moment.
The first time you see one of these things you make a statement that you know is incorrect, not factually true, and doesn't correspond with the real world, it feels like being thrown off a cliff. We must learn to reconcile these imperfections with the usefulness of the tools.
LTG: Can you share a genealogy use case from a newspaper article?
Steve:
Let’s say you found a newspaper article from a hundred years ago, and it's a long profile of one of your ancestors. For example, imagine you are related to Charles Lindberg, and you discover a 20-page magazine profile of Charles Lindberg. You could give the language model that 20-page profile and tell it to extract every name that's mentioned in the profile and include how the named person is related to Charles Lindberg. Were they a family member? Were they another pilot? Were they a business partner? Were they someone Lindberg met on the street? 
What you might ask the model to do would be, “Look at this article, generate a list of people mentioned in this article, and tell me their relationship to Charles Lindbergh.” And then you realize, well, gosh, now I'm going to have to go through and read the whole article to verify the relationship (not that reading the article is such a bad thing).
But here is a tip: have the chatbot help you with the verification. You could say, “Add a third column, after the name in the first column and the relationship in the second column. Now add a third column, showing and quoting to me the exact sentence that led you to presume or make the claim that Bob Smith is Charles Lindbergh's nephew. Give me that sentence and tell me what page number that's on.” And the language model will process the 20-page profile in moments, creating a spreadsheet with names, relationships, and quoted evidence.
LTG: How can genealogists collaborate with GPT to help the entire community?
Steve:
Collaborative learning is huge, and there's a long history of technology, of people sharing ideas and learning together.
Once you figure out how to do a task, like analyzing an obituary, you can create a tool and save your prompt. That way the prompt is ready for you next time you need to analyze an obituary. You don’t have to recreate the wheel. OpenAI calls these saved prompts a custom GPT.
So, a custom GPT is a way to save your prompts and to share your prompts. So, for example, if you and I process obituaries all day, and you tweak your obituary prompt so that it does something a little bit better, then we share that. And now we both have that better prompt. That's what these custom GPTs do. It's a way of sharing prompts. Like adding useful, helpful little tools to a Swiss Army knife. You discover a small task where the language model is helpful, efficient, or timesaving, then you save and share that use case prompt. And the whole community benefits. That is one of the purposes of my site AI Genealogy Insights.
GeneaGPT and Custom GPTs: How they work
LTG: Tell us about Open GeneaGPT.
Steve:
Open GeneaGPT is a long prompt I’ve created where I've asked the AI to pretend to be a genealogist. It's just a prompt that says, “You are a professional genealogist. You know about the genealogical proof standard. Users are going to ask you questions about genealogy; answer them as if you were a professional genealogist. And adhering to the genealogical proof standard is important to you in your response to a user.”
In its short-term memory, it says, “Okay, I'm a genealogist.” When you ask it a question, for example, “How should I get started in genealogy?”, it consistently says, start with yourself. Start from what you know, which is basic genealogical advice. How many blog posts and glossy genealogy magazines, and how many podcasts and how many workshops have you gone to where they say, start with yourself and work backwards. Open GeneaGPT offers basic help like that.
LTG: How can other genealogists access these Custom GPTs?
Steve:
Right now, only one company makes this available to share these prompts, and that is OpenAI with Explore GPTs. It’s like a free app store for custom GPTs. You get access to custom GPTs and the ability to create and save these little tools.
Other companies are also adding more AI tools. Microsoft gives you free access to OpenAI's tools. Within Microsoft's Bing browser, you can access Copilot, the Microsoft AI. In all your Microsoft products such as Excel, Word PowerPoint, in the Bing browser, you now have a Copilot button.
OpenAI has a GPT store where there are now tens of thousands if not millions of these tools. People have found use cases, and they're sharing them with others. We don't have to reinvent the wheel.
For example, there is one tool that uses image analysis; you can open your refrigerator and take a picture of what's in your refrigerator, and this custom GPT will suggest to you what to cook for dinner based on the ingredients that it identifies from the image of your refrigerator. It looked and it said, “Oh, there's some hamburger, and egg, and there's an onion. What can we do with these ingredients? Let's make meatloaf!”
LTG: What are AI tools best suited for?
Steve:
The four basic transformations especially useful to genealogists performed by language model tools like ChatGPT are summarization, extraction, generation, and translation.
Summarization
Summarization is when you take a lot of words, and you condense them, like transforming a lump of coal into a diamond. You take a page worth of a biography, and you distill it to a tweet – and it's exquisite, it's poetic, it is worthy of a haiku. These things are very good at language. If you were to give it the obituary of Charles Lindbergh and say, reduce this to an award-winning haiku, it would. Summarization is taking a lot and making it smaller.
Extraction
Extraction is finding a needle in a haystack, but it's more like finding a golden needle in a field of haystacks. It's like saying, go out and find all the mentions of this name.
Generation
Language models are very good at generating text. It's good at taking a little bit of words and spinning that out into a lot of words. For example, this is your report generation. This is where you start with a list of names, dates, places, events, and relationships. That's genealogical information. That's a little export from your genealogical database. For example, ”Here's John Smith's family – a little list of names, dates, places, relationships, and events. Turn that list into a narrative report, a poem, a short story, or a genealogical biographical report. And only use the facts from this list.” And it will generate text exactly matching your prompt instructions.
Translation
The last basic LLM transformation is translation. And that's much more than translating human languages. It will translate from one human language to another with caveats. It's not perfect. It's very good. It's as good as any other computer translation out there. But it’s not better than a paid translator. However, if you don’t speak both languages in the translation, the source and the target language, you may need an outside validator to verify it. This saves time as the translator then only needs to validate rather than create the original translation.
However, translation is much more than French to English. It also means you can take something like Shakespeare, and you can translate Shakespeare into contemporary English. If you have an old will, it can translate legalese into plain English. Or you could take a modern-day contract and say, help me understand.
It could also translate 17th century legalese. If you had a 17th century deed written in yards and chains and metes and bounds – things that beginners may not understand – you could translate the deed into plain English. It can also translate from a scholarly article. For example, if you have an article about genetic genealogy and you want to say, “Explain this to me like I'm a fifth grader, or I’m a 10th grader.” It can translate it in a way that is easier to understand.
And it will even take a bullet point list. You could just jot down a handful of notes, and you could say, “Take these handful of notes and spin this up to a business proposal or an email or a letter or report or a memo to file, or anything.” And so that's another form of translation.
And the last way that it's important to understand translation is as a very good editor. You can give it a very rough first draft, and you could say, “Translate this from a rough draft into standard written professional business English.”
AI for Translating Handwriting in Genealogy Research
LTG: Can the AI tools today read handwriting?
Steve:
These tools are getting better at reading handwriting, and they can read block, handwritten block print. Ultimately, though, we want handwritten text recognition (HTR) to be as good as speech recognition. By comparison, right now, we can talk to Siri or Alexa, and they are very accurate. Handwriting text recognition is not where speech recognition is.
We all want handwriting to be where speech recognition is. We want it to just work the way speech recognition works. We want to show it a letter from a grandparent that it's never seen before and see it accurately recognize the text. But right now, that's not how it works. Today, you must train AI systems to understand your grandmother's cursive handwriting. And just because you teach it one grandmother's cursive handwriting, you've got other grandmothers, and her handwriting may not look the same as this one.
HTR systems are getting closer, but our expectations are through the roof. We want to give them the hardest problems, writing that none of us can visually figure out. We want the computer to do it. That’s a little beyond what they're up to. But it is coming eventually. At least, that is the hope and expectation. But we’re not there yet. Handwriting is hard.
LTG: How do you anticipate these tools impacting the efficiency in the workplace, and specifically for genealogists?
Steve:
They will make us much more efficient. You’ll be able to get more work done in 40 hours than you did before. Or you could say, I did my 40 hours’ worth of work in 35 hours, and now I've got five extra hours. I can either do more work or I can spend five hours with my family.
We’re in an age of discovery today. Many people are trying different things, to find out what tasks these AI tools are good at. And once one genealogist finds a way to use these tools, and then they share that successful task with other genealogists, those accomplishable tasks are called use cases. And so, we all get smarter, better, and faster. But to get there, often we try things that don't work. Sometimes we'll try to use AI to do something, and it may or may not do it. And even if it does it, there may still be an old school way that's better. So right now, folks are trying things, and sometimes they work, but that doesn't always mean it's better. Sometimes the old school way just works better. And there are many, many tasks that AI is close to doing reliably now, and in six months and six years, AI will be able to do more. We just don't know how long it will take to accomplish any specific task. This uneven advance is called the jagged frontier.
For the folks who want to participate in using and learning AI, it's going to give them superpowers. It's going to make you better and faster and quicker at your job if you're a knowledge worker.
Implementing AI will lead to an opportunity for company growth as well. Chief Executive Officers will say, “I'm going to keep one hundred percent of my people and we're going to do 150% of the work that we used to do. We're going to grow our business. We're going to hire more people because we're better at this than anybody else. For example, we can do genealogy better, faster, and cheaper than our competition because we've trained our 75 researchers how to use these tools to produce twice the work in the same amount of time.”
Read part one of this two-part series here.
At Legacy Tree Genealogists we focus on the best possible results for our clients and are constantly seeking out new tools and resources, such as the use of AI, to make our time more efficient and effective. Are you ready to hire a genealogist? Reach out for a free quote and get started today.
Leave a Reply