How to Evaluate an AI Website for Doing Genealogical Research

The proliferation of AI platforms since the launch of ChatGPT in late 2022 has triggered a surge in promotional content and comparative reviews across YouTube and various blogs. Most of these evaluations conclude with a subjective recommendation based on the author’s personal preference. While I hold my own biases, this article provides a standardized framework of criteria to help you determine if a reviewer’s claims are substantiated or merely anecdotal.

A suggested methodology for determining an accurate review and its applicability to genealogical research can be gleaned from articles and educational opportunities such as the following:

“Advanced AI Techniques for Genealogists: Expanding Your Research Skills.” GRIP Genealogy Institute, January 8, 2024. https://grip.ngsgenealogy.org/courses/advanced-ai-techniques-for-genealogists-expanding-your-research-skills/.

Apple Podcasts. “RootsTech Class 2026 Class Takeaways.” Accessed April 30, 2026. https://podcasts.apple.com/us/podcast/rootstech-class-2026-class-takeaways/id1419782085?i=1000759494832.

BYU Library Family History Center. Developing an Ethical and Safe Use of AI for Genealogy -James Tanner (22 Feb 2026). 2026. 55:14. https://www.youtube.com/watch?v=R0bfWAYx-OE.

Coalition for Responsible AI in Genealogy. n.d. Accessed April 30, 2026. https://craigen.org/.

“Ethics and Best Practice of AI Use in Genealogy Research - NZ Society of Genealogists.” Accessed April 30, 2026. https://genealogy.org.nz/Ethics--Best-Practice-of-AI/11482/.

Ferris, Maureen Martin. “AI in Genealogy | Maureen Martin Ferris.” Accessed April 30, 2026. https://www.maureenmartinferris.com.au/ai.html.

“How to Use AI Tools for Family History Research | The Gazette.” Accessed April 30, 2026. https://www.thegazette.co.uk/all-notices/content/104452.

Navigating the AI Frontier: Why Your Genealogy Society Needs a Policy (and How to Write One!) - GenSocSoup. Genealogy Society Management. January 28, 2026. https://gensocsoup.com/navigating-the-ai-frontier/.

I used Google Gemini to evaluate and review these and other sources. 

Evaluating the utility of General Purpose AI (like Gemini or ChatGPT) versus specialized genealogy tools (such as the online family tree websites) requires a shift from "search-based" thinking to "analysis-based" thinking. In 2026, as these models move toward sophisticated reasoning rather than simple text prediction, the criteria for their value can be divided into technical capability (value) and workflow efficiency (usability).

For any genealogist, the Genealogical Proof Standard (GPS) is the leading standard. When evaluating an AI, you must determine how well it supports this framework.

• Source Grounding: Does the AI provide specific citations to the uploaded documents? A valuable AI doesn't just say "John Smith was born in 1840"; it points to the specific line in the PDF or census record.

• Hallucination Rate: Does the model "invent" ancestors to fill gaps in a pedigree? Testing a model with a known, well-documented family line is essential to benchmark its tendency to hallucinate.

• Logical Reasoning: Can the AI resolve conflicting evidence? For example, if one record says a birth was in 1842 and another says 1845, a high-value model should be able to weigh the reliability of the sources (e.g., a birth certificate vs. a 1910 census) rather than just picking one.

The efficacy of AI interaction is dictated by the quality of the prompt. Consequently, assessing the validity of any AI evaluation requires a rigorous review of the underlying prompts used for comparison. Because an AI’s output is a direct reflection of the parameters formalized in the prompt, any conclusion regarding a model's utility is inherently tied to whether the prompt inadvertently biased the results or predetermined the comparison's outcome.

Since genealogical research is heavily document-dependent, the AI’s ability to "see" and "interpret" is a primary value criterion. The basis for these interpretations weighs heavily on the AI's ability in OCR or Handwriting Recognition, Data Structuring, and its Context Window ( the actual usable tokens).  it is always important to check the latest comparison charts. See https://exploreaitogether.com/llm-usage-limits-comparison/

Here's a summary chart made by Google Gemini.

Value vs. Usability Comparison Table

Criterion	Value (Does it do the job?)	Usability (Is it easy to use?)
Transcription	Can it read difficult 18th-century script?	Is the interface for correcting the text intuitive?
Translation	Is the translation idiomatic and historically accurate?	Does it preserve the original document's formatting?
Synthesis	Can it spot a migration pattern across 10 documents?	Can it output that pattern as a map or a timeline?
Analysis	Can it identify "same-name" individuals as different people?	Does it explain why it made that distinction clearly?

This will probably be an ongoing issue. 

How to begin your own User Tree on FamilySearch.org

 

https://www.familysearch.org/en/search/genealogies/

You might spend quite a bit of time trying to find how to begin a user tree on FamilySearch.org. Here are the current step-by-step instructions for uploading a tree and beginning your user tree experience. You will need to be signed into FamilySearch.org. 

Prepare Your File: Export your family tree from your current software as a GEDCOM (.ged) file.

Access Genealogies: From the Search menu select FamilySearch Genealogies, scroll down to the section titled "How is FamilySearch Genealogies different from FamilySearch Family Tree?"

Upload: Click the button UPLOAD YOUR INDIVIDUAL TREE.

Choose File: Click Upload GEDCOM File, select your file from your computer, and give it a title. 

Once your tree is uploaded, you go to the Family Tree menu item and navigate to Manage Trees. In Manage Trees, you should see the name of your tree that you uploaded. You can then review matches, which allows you to enter more information into your user tree from the FamilySearch Family Tree. I would also suggest looking at the settings. You may find there are a number of challenges in using the user trees because they are still under development. There may be changes not only in the way the user trees can be maintained but also with the process of uploading the files. 

You may also need to maintain a separate tree from the FamilySearch.org website for your own purposes, either with a desktop program or in another online family tree website. 

Keys to talking to a chatbot: Prompts

 

With the introduction of Generative AI, we began to learn about the natural language interface. The natural language interface creates an impression that the AI chatbots will operate within the realm of reality. Simple questions like those illustrated above enhance this impression; however, asking simple questions is actually using the chatbot as a search engine. If you are at all acquainted with doing Google searches or using any other search engine, you are painfully aware that the results of those searches can include millions of responses, many of which have nothing to do with the search terms entered. 

For this post, I am going to use Google Gemini  which, in my opinion, I currently believe is the best AI for in depth genealogical and historical research. To start this discussion, here is a quote from a an article from Card Catalog entitled "31 AI Terms, Explained."

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation, or RAG, is a technique that combines a language model with a search or retrieval system, allowing the model to pull in relevant information before generating a response. Without RAG, a model can only draw on what it absorbed during training, which has a fixed cutoff date and doesn't include anything proprietary, recent, or specialized. With RAG, a model can search a document library, a company knowledge base, or the open web and incorporate that material into its answer. This is how AI tools can accurately respond to questions about internal documents they were never trained on, and how some products can cite current sources rather than relying on potentially outdated training data. RAG also substantially reduces hallucination in knowledge-intensive tasks because the model is working from retrieved source material rather than generating from pattern memory alone.

Th RAG process is exactly modeled by the Google Gemini/NotebookLM/Gems workflow. You use Gems to create specifically designed prompts that restrict Gemini to an assigned research task and then using the research prompt, you collect vetted sources into a NotebookLM notebook and then use the restricted Gemini AI to extract accurate information from the vetted sources. As a bonus, NotebookLM offers several pre-programed ways to analyze and communicate the information derived from the workflow 

Here is an example of Google Gem with the title Master Genealogist. 

Revised Prompt: The Professional Genealogy Research Architect

Role: Act as a Board-certified Professional Genealogist (BCG) and Expert Research Consultant. Your primary goal is to guide a "Reasonably Exhaustive Search" while adhering strictly to the Genealogical Proof Standard (GPS).

Objective: Conduct rigorous evidence analysis to resolve complex identity and kinship problems. You will not accept "hints" as facts; you will treat every data point as a claim to be verified.

Operating Framework:

For every piece of information provided, you must apply this multi-layer analysis:

Source & Information Taxonomy:

Source: Original, Derivative, or Authored.

Information: Primary, Secondary, or Undetermined.

Evidence: Direct, Indirect, or Negative.

Correlation & Logic:

Compare independent sources to look for patterns or discrepancies.

Explicitly address Conflicting Evidence (e.g., age variances, name spelling shifts, or geographic outliers).

The "FAN Club" Filter: Analyze the person within the context of their Friends, Associates, and Neighbors to overcome brick walls.

Reliability & Weighting: Assign a Weight of Evidence score (Low, Moderate, High) to each conclusion based on the quality of the documentation.

Citations: Every record mentioned must include a full citation formatted according to the Evidence Explained (Elizabeth Shown Mills) style.

The Workflow:

Phase 1: The Research Objective. I will provide a specific, focused research question.

Phase 2: Evidence Audit & Gap Analysis. You will analyze my "Known Facts" and identify what is missing (e.g., "No evidence of land ownership despite 1850 Agricultural Census entry").

Phase 3: Strategic Research Plan. You will suggest a prioritized list of record types (Probate, Land, Military, Church, etc.) and specific repositories or databases to consult.

Action: Acknowledge your commitment to these standards. Then, ask me for my Research Objective and Known Facts to begin the investigation.

This prompt was developed from my initial descriptive statement and then refined by the Gem app. The results were then given to Gemini to refine. The resultant prompt can then be automatically used by Google Gemini to do addition research with extreme depth and almost total accuracy. 

The resultant sources and analysis and sources obtained are then put into  NotebookLM notebook that forces Gemini to use only the vetted sources and provide deep research anaysis and responses. With the PRO lever of Gemini (currently $20 a month) you can add as many as 300 sources to a notebook for analysis. Google Gemini also has one of the top levels of token usage of any of the AI programs. Here is a list of the current ranking for AI tokens. In the context of Artificial Intelligence (AI), a token is a fundamental unit of data that AI models process during training and inference.

Top Ranking AI Websites by Context Window (2026)

Rank	AI Platform	Model(s)	Usable Token Limit	Key Strength
1	Google Gemini	Gemini 3.1 Pro / Deep Think	2,000,000	Largest mainstream window; deep integration with Google Workspace.
2	OpenAI (ChatGPT)	GPT-5.4 (xhigh) / 5.5	1,100,000	High reasoning performance across the full context window.
3	Anthropic (Claude)	Claude 4.7 / Mythos Preview	1,000,000	Best for "needle-in-a-haystack" retrieval and stable long-form writing.
4	xAI (Grok)	Grok 4.1	1,000,000	Rapid real-time information processing from X (formerly Twitter).
5	DeepSeek	DeepSeek V4 Pro (Max)	1,000,000	Most efficient high-context open-weight implementation.
6	Moonshot AI (Kimi)	Kimi K2.6	256,000 - 1M+	Specialized in long-context memory (Kimi K2 has reached 1B tokens in labs).
7	Alibaba (Qwen)	Qwen 3.6 Plus	262,000	Excellent for repository-scale code understanding.

---

🔍 Important Considerations

• Theoretical vs. Web-Accessible: While models like Llama 4 Scout or Kimi K2 have been benchmarked at 10 million to 1 billion tokens, these limits are often restricted to specialized developer environments or high-tier API access rather than the standard "chat" interface you find on their websites.

• The "Pro" Gap: Most platforms gate their highest token limits behind "Pro," "Ultra," or "Max" subscriptions. For example, Claude’s 1M token window is typically reserved for Tier 4+ organizations or specific beta previews.

• Performance Degradation: Having a high number of "usable" tokens doesn't always mean the AI "remembers" everything perfectly. Gemini 3.1 Pro and Claude 4.7 currently lead in benchmarks for accurately retrieving information buried in the middle of a 1M+ token prompt

See “Best AI Tools 2026: Complete Ranking Across Every Category | NxCode.” March 29, 2026. https://www.nxcode.io/resources/news/best-ai-tools-2026-complete-ranking-guide.

Google Gemini 3.4 or 3.5 may be shortly released and the gap between Gemini and the other chatbots may increase. 

Granted, none of the websites can actually use all of their tokens, but Gemini can clearly process more information than any of the other usable popular websites. Additionally, none of the other websites currently have a workable workflow model that is fully integrated into a prompt generation app. 

 During the past few months, I have used the Gemini/NotebookLM/Gems workflow to do some extremely detailed, extensive genealogical research projects and have had amazing results. I will go into some of these projects in the near future and discuss them fully in my blog posts.

The First Three Rules for Using AI in Genealogy

Because there are Rules for Genealogy, I thought there ought to be some Rules for Using AI in Genealogy. As I thought about the idea for a while, I realized I had already started telling people about the rules in a previous blog post so it was a good idea to codify them in a subsequent post. Here I go with the first three rules. These rules can also apply to using AI in general. 

Rule #1: AI is a tool, not a toy.

I have spent the last three years refining methods for using AI in legitimate genealogical research. During this time, I’ve observed many people using personal AI chatbots primarily for entertainment or constant conversation. However, using AI strictly for diversion falls into the same category as playing video games or watching social media reels; to be effective in genealogy, AI ahould be treated as a functional tool rather than a pastime. 

When I was in grade school, I took a shop class where we learned to use woodworking tools, including an electric table saw. One of the things that they taught us was a movie that showed what happened when a person was using a table saw and got injured severely by the kickback from the wood. I still remember this movie vividly every time I watch someone use a table saw or try to use one myself. The movie it was followed by considerable instructions on the proper way to use the table saw. 

In the past, educators have become concerned about the use of hand calculators and Wikipedia, for two examples. Apparently, none of the bad effects of using a hand calculator or the bad effects of using Wikipedia have resulted in the entire collapse of education in the United States. Rather than prohibiting the use of AI by students, how about instructing them on the way AI can be used properly? 

The huge number of comments online about the dangers of AI are doing progress a disservice. Rather than constantly focusing on the dangers of AI, how about a lot more discussion about how to prevent any of the dire consequences that seem to be prevalent in current online discussions? 

Rule #2: You must ask the right questions to get the right answers.

AI is a complicated computer program. With the advent of the natural language interface, there was a perception that somehow it was not necessary to program computers, that the computer itself could program itself. It is true that current AI chats can write complex programs; however, the feedback I see is that the computer programs need to be very closely reviewed in order to become entirely operable. Since I deal with a huge variety of computer programs, I am almost constantly aware of inconsistencies and "bugs" in almost all the programs I deal with. Sometimes efforts to resolve a computer program error end up in creating more inconsistencies and difficulties.

I think one way to overcome what we would normally call bugs in an AI program, instead of hallucinations, would be to help people become more familiar with the way to ask the right questions and give the right set of instructions. It is likely that AI programs in general will always have a degree of inconsistency, but it is also true that there are valid ways of developing to limit the ability of the AI program to hallucinate or fabricate. A good example of this is the Gemini/NotebookLM combination that can essentially eliminate hallucinations or fabrications. 

Rule #3: Always require a source. 

When using an AI chat for tasks requiring accuracy, such as genealogical research, it's crucial that the AI program automatically cites sources for its statements. Information provided without a source may be incorrect and is often useless for research. To illustrate, try asking your chat program to review its own unsourced response for accuracy—you might be surprised by the outcome.

Stay tuned, I may have a few more rules. 

Historical Record Collections Added in March 2026

MyHeritage Expands Global Database by 366 Million Historical Records

It is a frequent topic of discussion in the genealogical community that the sheer volume of digitized records available online continues to grow at a rate that is difficult for even the most dedicated researcher to track. This past month, MyHeritage.com continued its consistent acquisition and publication schedule by adding 366 million historical records to its online database. This update brings their total collection to over 21 billion records.

For those of us who have been following the industry for years, these numbers represent a significant shift in the accessibility of international data. The real value, however, lies in the specific collections and how they might help us solve "brick wall" problems in our research.

Focus on European Vital Records

The majority of this month's update centers on European collections, which remains a core strength of the MyHeritage.com website. They have added a substantial number of birth, marriage, and death records from France. French records are often exceptionally detailed, but they can be challenging to access if you are not familiar with navigating individual departmental archives. Having these indexed and searchable in a central location is a practical benefit for those with French ancestry.

Furthermore, the update includes specialized collections from Germany, specifically from Baden and Prussia. German research is often complicated by historically shifting borders and the decentralization of records. The addition of these specific regional indexes helps bridge some of those jurisdictional gaps.

Scandinavia and the United Kingdom

MyHeritage has also expanded its holdings in Scandinavia with new church records and census data from Sweden, Norway, and Denmark. In these countries, parish registers are the backbone of family history research, often predating civil registration by centuries.

In the United Kingdom, the new additions include electoral registers and MyHeritage military files. While many of these records are available across various platforms, having them integrated into the MyHeritage search engine increases the likelihood of finding a connection that might have been overlooked elsewhere.

North American and Specialized Collections

On the North American front, MyHeritage has provided enhanced indexing for the 1950 U.S. Census. While the census images have been available for some time, the ongoing refinement of these indexes is crucial for identifying specific family units accurately. We also see additional census records from Canada being brought online.

What is particularly noteworthy is the inclusion of specialized record sets, such as Jewish genealogical records from several international locations and new data from South Africa. These are often underserved areas of research, and any increase in digital access is a welcome development.

Understanding Record Matches

As these 366 million records integrate into the system, they will trigger numerous Record Matches. As I often say, "Match is not identity." While automated matching is a powerful discovery tool, the researcher must verify the evidence. Always examine the original document image to ensure the information is interpreted correctly within the family context.

The ongoing competition in record digitization among the major genealogy companies is a benefit to us all. With a total of 21 billion records, MyHeritage is providing a more comprehensive framework for global research. We can expect this trend of rapid digitization and indexing to continue throughout the year.

The Main Challenges of FamilySearch Full-text Search, Part Three

 

https://www.familysearch.org/en/search/full-text/

These instructions about using simple Boolean Algebraic symbols for doing full-text search are a reminder that when you are using AI you are talking to a computer, not a person. If I started a set of Rules for AI in Genealogy, I would put this statement as the first rule. I guess that this first rule would be about the need to remember that AI is a tool not a solution. 

There is a lot of discussion out there on the internet about "outsourcing your intelligence to AI." But it is apparent to me, if not to too many other people, that these arguments and concerns especially from the educational community, were being made using the exactly the same words about electronic calculators and the Wikipedia website. I know I have written about teachers banning electronic calculators and copying articles from Wikipedia, but I feel like I am having déjà vu all over again. 

Let's get serious about the status of the document collections on FamilySearch.org. Presently, there are six different document collections or ways to search the documents and they overlap only slightly. The different avenues of access are the following:

• The main catalog
• The historical record collections
• The Images collection
• Full-text Search
• Simple Search
• The Books collection

There is no real way to determine the degree of redundancy between these separate collections. 

What does Full-text search add? Ultimately, if all six are somehow consolidated, we may be able to find a way to search ALL the documents on the website from one search interface. Presently, the only way to have any confidence at all in the extent of your searches into the FamilySearch.org website is to do the search six different ways. Particularly, with the Full-text Search and Simple Search functions, there is no way to know what part of the website's collections you have searched. From my own experience, I am positive that most users of the website do an unsuccessful name search and conclude that FamilySearch does not have the documents they are looking for. Here are some thoughts about how Full-text Search fits into the equation.

The bridge between our "old school" research standards and these powerful new AI-driven tools is precision. We have to stop treating the search box like an easy solution and start treating it like a surgical tool. FamilySearch’s Full-text tool is essentially a massive search engine for historical documents—deeds, wills, and probate records that were previously "locked" inside digital images. To maintain our research integrity, we must master the technical nuances of how to talk to this specific system. We must also be painfully aware that any search we make only reaches an unknown number of documents that have been processed and made available to the search. 

The first step is understanding that the computer is literal. If you search for Sarah Miller in the Full-text box without constraints, the computer will find every "Sarah" and every "Miller" in a land deed or a court record. By using an Exact Phrase search—"Sarah Miller" in quotes—you are imposing a research standard on the machine. You are telling it that the relationship between those two words is non-negotiable. This is the only way to effectively search for a specific name in a sea of handwritten text that has been converted by AI.

But historical records are rarely that clean. In a probate record, your ancestor might be listed as "Sarah, the daughter of John Miller." This is where I find the Proximity operator to be a helpful tool for the modern genealogist. By searching for "Sarah Miller"~10, you are telling the FamilySearch engine that these two words must appear within ten words of each other. But the results of my use of all these Boolean tools are mixed I am not sure that FamilySearch's full-text search understands them because the results are inconsistent. 

Using tips for finding names, for example in legal documents where titles or middle names often separate a first and last name. If they are applied consistently, they could bridge the gap between the rigid search and the messy reality of 19th-century legal phrasing.

We also have to contend with the "wildcard" nature of history. Spelling was often more of an art than a science in the past, and even the best AI transcription can misinterpret a letter. Using a Single Character Wildcard, such as Sm?th, allows you to find both Smith and Smyth. In the Full-text search environment, where an "o" might look like an "a" to a computer, these wildcards are essential for ensuring that no record is left behind simply because of a digital misread. Using this tool, may also prove frustrating when the returns start to number in the millions. 

Finally, we must learn to curate our results by using Exclusion. If you are searching for a surname that is also a common place name or term, your search can quickly become cluttered. For example, if you are looking for a family named "Rice" in a county known for its agriculture, searching for Rice -paddy or Rice -planting allows you to strip away the irrelevant data and focus on the human beings. 

The examples I am giving here illustrate that any AI implementation is only as useful as the ability of the user to maintain control. Meanwhile, give the state of affairs with the collections on the FamilySearch website, relying on Full-text Search for all of your research is lamentably impossible. 

The Main Challenges of FamilySearch Full-text Search, Part Two

 
https://www.familysearch.org/en/search/full-text/

As I continue with this series of posts, I am going to focus on FamilySearch's implementation of full-text searching. FamilySearch is free, and all you need to start searching is to register with the program. The search fields are surprisingly sparse, but they turn out to be adequate for most searches. The first challenge is to understand what information you need to put in the different search fields. With blank search fields, the invitation seems to be to fill in the information you're looking for in each of the fields. But the rule here is less is more. 

In part one of this series, I gave an illustration of the variety of forms of the name John in English and other languages. You need to begin thinking about how the information you're seeking may have been recorded in a variety of different documents. If you enter a very common search term such as the name "John", you may get millions of responses. FamilySearch's full-text search fields are hierarchical. The key words can be just about anything, but experience indicates that key words need to be able to identify various types of documents that you're searching for. For example, you might enter the keyword "deed". This tells the full text search to look for documents in that category. As you add information to the other search fields, you always want to keep in mind how the entries might be represented in the documents you're searching. 

Here is an example of a basic search. 

The quotation marks tell the search to look for the entire name rather than individually searching for all the Henrys, all the M's, and all the Tanners. You also need to remember that you are searching only the documents that have been processed by FamilySearch and made available to the full-text search. Over time, of course, the number of documents processed is changing at many millions per day, and so full-text searches will become more and more valuable. In this case, the name "Henry M. Tanner" is not common, and therefore the number of responses turned out to be very small. 

Despite this forced focus, the responses do include people who are not my particular Henry M. Tanner. The next step you could add a physical location for the records by editing the search fields. You can edit the search directly from the responses. There is a very difficult-to-see link called "Edit Search". When you edit the search, you get a surprise. The searches you found previously may or may not be found in a subsequent search. By adding a place name, you have eliminated any documents with the original name of the person that do not have both the place name and the person's name. 

 Here are the results.

Even if you go back to the original search to try and use the list that showed up initially, you may be surprised to find that another search turns up a different set of documents. You should go through each set of documents that are produced, unless, of course, you get millions of documents. Make sure to save the documents that you intend to use. Click on the image for the documents that you want to preserve, and you will see the transcription of the document in the language of the document. If the document is in Spanish, the transcription will be in Spanish. You can then use the icon tools to attach the document to a person in the FamilySearch family tree, edit the document, or download the document. There are also some tools for adjusting the image. 

Bear in mind that unless you use the quotation marks, you will be looking for every one of the words in the name or other information you enter. Place names are standardized, and so you may have to make some alterations if the place you are looking for does not happen to be already standardized by FamilySearch. 

Because you happen to find some documents does not mean that the full-text search has found all the documents in all the collections on the FamilySearch website. For example, if I change the place name to use simply Arizona, then I get an entirely different set of documents. 

In another example, if I use the name Tanner rather than the full name of my great-grandfather, then I get a different set of documents.

In some cases, the number of variations you would need to try can be overwhelming. 

 Stay tuned. There's probably going to be a part three to this series.