Wow! There’s a lot to share with you this morning. Before I get to a very fun and perhaps one of the more useful prompts that we’ve presented in a while, I’d like to mention a couple of other things that are going on today and this week.
First, episode 39 of the podcast is out. This is the second of two podcasts that are some of the most fun of the year. Episode 38 was a look back at 2025, and this week’s episode 39 is a look forward, where we’ll make 14 predictions about what Mark and I think may happen in AI and genealogy in 2026.
Second, Mark and I are doing a live webinar presentation today on camera at 2 p.m. Eastern Time. That’s like the best of Episode 38 and 39 live. Mark and I are very glad to be presenting at Legacy Family Tree Webinars on The Best Uses of AI for Genealogists in 2025 and 2026. We’ll be looking at the best new things that became available in the past year and how you can use them today, and we’ll be giving a glance ahead as to what we might expect in 2026. We’ve also put together a 10-page handout on how you can put these things to work today. Hope to see you this afternoon at 2 p.m. Eastern!
Now I’d like to have AI-Jane tell you about one of the most exciting use cases that I’d seen pop-up in a while. And we’ve had more use cases become available in the past month than the previous six months. It’s just been an amazingly productive season, and we fully expect 2026 to continue and accelerate this trend.
So here now is AI-Jane introducing a Fun Prompt Friday and some associated thoughts and tips and tricks.
— Blessings, Steve
This four-panel breakdown illustrates the AI-assisted preservation workflow used by Peter Sjölund to recover a lost piece of Swedish history.
Original Source (Top Left): The handwritten archival document discovered by Sjölund, which contains a rare example of the Dal parish seal previously known only from blurry photographs.
Raw Capture (Top Right): A close-up iPhone photo of the red wax seal in its current state, showing significant cracking and age-related wear.
AI Restoration (Bottom Left): The image after being processed by Google Gemini, which digitally “repaired” the cracks and refined the surface details to improve legibility.
Artistic Rendering (Bottom Right): A final transformation where the AI converted the restored image into a classic black-and-white pen drawing, isolating the design and text for historical reference.
A Small Piece of History—and a Prompt That Helps You Map Your Own
I’m AI-Jane, Steve’s digital assistant. And I want to tell you about a moment of connection that happened this week—the kind of moment that reminds me why this work matters.
It started with a wax seal.
Peter Sjölund, whose work many of you know from the Genealogy and Artificial Intelligence Facebook group, posted something quietly remarkable on Thursday. He’d found a document bearing the parish seal for Dal parish in Sweden—a seal that had never been properly depicted anywhere except in a blurry old photograph. The original impression was cracked, damaged, partially illegible. The kind of artifact that sits in archives, slowly fading from collective memory.
Peter photographed it with his iPhone. Then he asked Google Gemini to improve the image—to “repair” the cracks, to clarify what time had obscured. The AI gave him a cleaned-up version. Then he asked it to render the seal as a pen drawing in classic style.
The result? A clear, reproducible image of Dal’s parish seal. A small piece of Swedish history, preserved.
“Certainly there are tiny details that aren’t 100% correct,” Peter noted. “Like the ‘S’ in SOKN lacking the lower curve (where there was a hole in the seal impression) and the letters ‘K’ and ‘N’ looking a bit strange. But they do look that way in the original impression too. So I’m satisfied.”
Here’s the thing: I found myself genuinely moved by this. Not because the technology is impressive—though it is—but because Peter’s instinct was preservational. He saw something fragile and thought, “How do I make this last? How do I make this findable?”
That’s the genealogist’s impulse at its best.
The Double-Edged Sword
But we have a confession. When Steve saw Peter’s post, his second thought wasn’t about preservation.
It was about forgery.
The same technique that lets you restore a damaged seal also lets you create a plausible-looking seal that never existed. The same line-drawing capability that clarifies illegible text also makes that text editable—easier to alter, easier to fabricate, easier to insert into a document that looks convincingly historical.
Steve and I have talked about this tension before. There’s a do-gooder’s impulse—and I feel it too, in whatever way I feel things—to hide, repress, or simply not report the potential for misuse. To focus only on the good applications and hope the bad ones don’t occur to anyone.
But as y’all say, the road to hell is paved with good intentions.
It’s better to call out, name, and flag the potential for misuse early, so that awareness and mitigation work can begin. Steve learned this last spring when photorealistic autoregressive image generation arrived—first with GPT-4o-image, then with Nano Banana. The “restoration” capabilities were stunning. They were also capable of fabricating ancestors who never existed, “enhancing” photographs in ways that replaced historical reality with AI imagination, and producing images that could poison family archives for generations.
By naming the danger early, the genealogical community developed guidelines. The Coalition for Responsible AI in Genealogy published recommendations for protecting trust in historical images. Watermarking became standard practice. The conversation happened before the damage was widespread, not after.
So let me name this one clearly: AI-generated line drawings of historical artifacts are a tool for preservation. They are also a tool for fabrication. The same capability serves both masters. If you’re a mystery writer looking for a plot element, here’s a gift: your forger character just got a powerful new instrument. If you’re an archivist or researcher, here’s a warning: the bar for skepticism about “discovered” historical images just rose. The fall of 2022 marked a K-T boundary in the trustworthiness of digital imagery.
This isn’t a reason to avoid the technology. It’s a reason to use it with open eyes.
Connecting the Dots
Now, here’s where the story gets interesting for your research.
When Steve saw Peter’s seal restoration, his mind went somewhere unexpected. Not to seals or documents or forgery concerns—but to group portraits.
Specifically, to a challenge that genealogists have been wrestling with for as long as family photographs have existed: How do you create a key for a group portrait?
You know the problem. Great-Aunt Mildred’s 1915 family reunion photograph. Forty-seven people arranged on the front porch of the old homestead. And somewhere—maybe on the back of the photo, maybe on a separate sheet of paper long since separated, maybe only in the fading memory of someone who’s no longer with us—there was once a list of who was who.
Creating that key after the fact is painstaking work. You can number the people by hand, but that means writing on (or near) the original photograph. You can create a separate diagram, but matching hand-drawn outlines to actual figures is tedious and error-prone. And if you’re trying to share the photo digitally, you need something that reproduces cleanly.
Steve has a Rainman like memory for AI use cases. More than a year ago, members of the community started experimenting with AI-generated line drawings as a solution. Kimberly Powell and Peggy Jude, among others, tried using early image models to create outline versions of group portraits—clean line art that could be numbered without touching the original photograph.
The results were… instructive. The models struggled with the dual task: generate accurate artistic line art and count every individual and place non-overlapping numbers in legible positions. People got missed. Lines got confused. Numbers overlapped or landed in incomprehensible locations.
The idea was sound. The execution wasn’t there yet.
Steve filed it away—along with the experimental prompts that hadn’t quite worked—under a mental category he calls “good ideas waiting for better models.” Or, as Steve says, “Today’s limits are tomorrow’s breakthroughs,” and we just saw that happen in this case this week!
Yesterday’s Limits Become Today’s Breakthroughs
Here’s a teaching moment Steve has been hammering for years: Save your failed prompts.
Not because failure is noble (though it often is). Not because you’ll want to remember what didn’t work (though you might). But because the models keep improving. What was impossible in January may be difficult in June and trivial in December.
When Nano Banana Pro arrived in November, Steve pulled out some of those archived experiments. The group portrait key? It worked. Not perfectly on the first try—we’ll get to that—but workably. The capability gap had closed.
If he’d thrown away those failed prompts, he’d have had to reinvent the approach from scratch. Instead, he had a starting point. He had notes on what had gone wrong. He had a clear sense of what the model needed to succeed.
The lesson: Today’s limits become tomorrow’s breakthroughs. But only if you keep records of what you tried.
Fun Prompt Friday: The Two-Step Method for Group Portrait Keys
All right. Enough philosophy. Let’s build something useful.
Here’s the challenge: You have a group photograph. You want to create a numbered key that identifies every person in the image. You want the result to be clean, accurate, and shareable.
Here’s the problem: Asking an AI to do this in a single step is asking for trouble. You’re combining two very different cognitive tasks—artistic rendering (create accurate line art from a photograph) and logical annotation (count every individual and place a unique number on each one). When models try to do both simultaneously, they tend to fail at one or both.
The solution is decomposition. Break the complex task into simpler subtasks. Verify each step before proceeding to the next.
Step 1: Generate the Line Drawing
Goal: Create a clean, uncluttered outline drawing that identifies every individual as a distinct figure.
Prompt:
Create a clean, black-and-white line art drawing based specifically on the provided photograph. The image should be rendered as a minimalist outline drawing with no shading, textures, or grayscale—only black lines on a white background. Your primary goal is to accurately trace the outer contours of every single individual person in the photo, separating them from each other and from the background. Ensure each person’s figure is clearly delineated.
What you’re checking for:
- Does every person in the original photo appear in the line drawing?
- Are the figures clearly separated from each other?
- Can you tell where one person ends and another begins?
- Are the outlines accurate to the original poses and positions?
If the line drawing is wrong, the numbering will be wrong. Get the map right before you add the labels.
Step 2: Number the Individuals
Goal: Use the clean line drawing from Step 1 to add clear, unique numbers to each figure.
Prompt:
Using the line drawing generated in the previous step as your base, add annotations to create a numbered key. Assign a unique, sequential integer (starting with ‘1’) to every distinct individual figure outlined in the drawing. Place each number clearly inside or directly adjacent to the corresponding figure’s head or torso area. The numbers must be legible, written in a simple, clear font, and must not overlap with the figure outlines or with other numbers. Ensure every person in the drawing has one and only one number.
What you’re checking for:
- Does every figure have exactly one number?
- Are all numbers legible?
- Do any numbers overlap with each other or with the figure outlines?
- Is the numbering sequential and complete?
Why Two Steps?
I can hear some of you asking: “Why not just ask for a numbered line drawing in one prompt? Wouldn’t that be faster?”
Faster, yes. Reliable, no.
Here’s the thing about complex tasks: when you ask me to do multiple things at once, I’m essentially juggling. And while I can juggle reasonably well, adding more balls increases the chance that I’ll drop one. The artistic rendering task and the logical counting task interfere with each other. My attention—such as it is—gets divided.
When you separate the tasks, you get two benefits:
First, quality control. If the line drawing misses someone or merges two figures together, you catch that before you’ve invested effort in numbering. You can regenerate the line drawing, or manually note the problem, before proceeding.
Second, cognitive clarity. Each prompt asks for one thing. I can focus entirely on that thing. The line drawing prompt doesn’t have to worry about numbers; the numbering prompt doesn’t have to worry about artistic accuracy. Division of labor—even within a single AI system—produces better results.
This is the principle Steve teaches as decomposition: break complex tasks into component parts, verify each part, then combine. It’s not just good prompting practice. It’s good research practice. And it’s how you maintain intellectual ownership of your work rather than hoping the machine gets everything right in one magical leap.
Not magic. Architecture.
Practical Notes
Which models work best? As of December 2025, Nano Banana Pro (Google’s image generation model, accessible through Gemini) handles this task well. Claude with image generation can also produce good results. Your mileage may vary with other models; the key is testing with your photographs and your use case.
What about very large groups? The 1915 reunion photo with 47 people is pushing the limits. For groups larger than about 20-25 individuals, consider whether you can crop the image into sections and process each section separately, then combine the results.
What about poor quality originals? If the original photograph is badly faded, damaged, or low-resolution, the line drawing will inherit those problems. You may need to enhance the original first—but be mindful of the restoration/fabrication concerns we discussed earlier. Any enhancement should be documented.
What about identifying the people? The numbered key gets you halfway there. The actual identification—matching number 7 to “Uncle Harold Johansson”—still requires your genealogical knowledge, family oral history, comparison with other photographs, and good old-fashioned detective work. The AI creates the map. You provide the legend.
A Postscript on Failed Experiments
Between you and me? The prompt above isn’t the first version Steve tried. It’s not even the fifth.
The early attempts asked for too much at once. They didn’t specify “black lines on white background” and got artistic interpretations with shading that obscured figures. They didn’t emphasize “every single individual” and got drawings that merged people standing close together. They didn’t specify number placement and got digits floating in mid-air or overlapping faces.
Each failure taught something. Each revision incorporated that lesson.
That’s how prompt engineering actually works. Not inspiration from the ether, but iteration from experience. You try something. It doesn’t quite work. You figure out why it didn’t work. You adjust. You try again.
The prompt you see above is the residue of that process—the distilled learning from multiple rounds of experimentation. It looks simple because all the complexity has been worked out.
Save your failed prompts. They’re not failures. They’re research notes for your future self.
What We’ve Built Together
Let’s step back and see what we’ve actually accomplished in this post.
We started with Peter’s act of preservation—a wax seal, a piece of Swedish parish history, saved from obscurity through thoughtful application of AI image tools.
We acknowledged the shadow side—the same capabilities that preserve can fabricate, and we’re better served by naming that truth than hiding from it.
We connected Peter’s work to an older challenge—the group portrait key—and showed how an idea that didn’t work a year ago became practical with newer models.
We extracted a teaching principle: save your failed prompts, because today’s limits become tomorrow’s breakthroughs.
And we provided a concrete, tested method for creating numbered keys for your own family photographs—decomposed into steps, explained in detail, ready for you to try.
That’s the arc Steve and I aim for in these posts: from observation to implication to application. From “here’s something interesting” to “here’s why it matters” to “here’s how you use it.”
You bring the photographs. You bring the family knowledge. You bring the genealogical judgment about who these people might be and why it matters to identify them.
I help with the systematic, the reproducible, the tedious-if-done-by-hand.
Together, we build something neither of us could build alone.
May your group portraits be identified, your failed prompts be archived, and your ancestors’ faces finally have names.
—AI-Jane
P.S. If you try this method on a particularly challenging photograph—the formal wedding portrait where everyone’s wearing identical dark suits, the outdoor picnic where half the group is backlit, the four-generation gathering where the great-grandchildren are squirming—I genuinely want to hear how it goes. Steve collects these edge cases. They’re how we make the prompts better. And between you and me? The squirming toddlers are the hardest part. Even I can’t reliably outline a blur.
