AI, Education, and Genealogy

 

Volunteering in a library in a large university, I have a significant amount of contact with university students. Since my primary activity is teaching and supporting other volunteer missionaries and patrons of the library, I also have a significant contact with a broad spectrum of ages and backgrounds. In this context, I am in a somewhat unique position to see the impact of AI both in and out of a university environment. Currently, three of the large online family tree database programs, Ancestry.com, FamilySearch.org, and MyHeritage.com, all have AI involvement. The university, however, has mixed reactions as an institution about the use and implementation of AI in the classrooms. 

It is interesting that the overall main reaction with all of the individuals that I interface with seems to be based on the initial announcements that were made about AI more than three years ago. There is a correlation between the reaction of most of the public to the FamilySearch.org website with concerns about the changes made to an open AI-based family tree and the consistent initial fear of AI hallucinations. Individuals associated with the University have a broad spectrum of response to and implementation of AI. This attitude is generally a reflection of articles such these. 

Knight, Will. “Using AI for Just 10 Minutes Might Make You Lazy and Dumb, Study Shows.” Tags. Wired, May 6, 2026. https://www.wired.com/story/using-ai-negative-impact-thinking-problem-solving-study/. 

AI Chatbots Could Be Making You Stupider.” April 20, 2026. https://www.bbc.com/future/article/20260417-ai-chatbots-could-be-making-you-stupider.

This list could go on and on. It has been interesting to me to note that this reaction to AI is almost exactly the same as the reactions to hand-held calculators. Here is a citation to a Masters Thesis on this topic. 

Banks, Sarah A. "A Historical Analysis of Attitudes Toward the Use of Calculators in Junior High and High School Math Classrooms in the United States since 1975." Master’s thesis, Cedarville University, 2011. See chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://files.eric.ed.gov/fulltext/ED525547.pdf

Here is a quote from this Master's Thesis"

"The onset of calculators initially brought concerns that this new technology was not fully understood nor would be appropriately utilized by educators and that the effects on students were unknown.  Future research studying how children were affected and the necessary changes to curriculum was recommended (“Math in the Schools,” 1975).  At this time, some educators feared that students would not be able to retain their knowledge of simple arithmetic if they learned to use a calculator before fully grasping basic mathematical concepts.   Other teachers, however, saw calculators as a chance to increase student motivation by using more “real-life” problems (Pendelton, 1975)."

Traditional genealogy involves a significant amount of drudgery such as untold hours of combing through old documents page by page, either on a microfilm viewer or in documents themselves in an archive.

Here is a not-so-hypothetical problem faced by many research genealogists. In doing research in a single parish in England or some other country, you find many people with exactly the same names. Separating out the individuals into families, into unique families, is a tremendous challenge. Over the past three years, I have begun to develop a methodology to use AI to solve this particular challenge. The results have been spectacularly successful. Now, because I have used AI rather than sitting in front of a microfilm reader or scrolling through endless digital documents, I am wondering if my brain is now fried. Or is it the other way around? My brain was being fried by sitting in front of a microfilm reader, and now I have time to think about actually working on the documents. 

AI is here to stay. It will only become more useful and more universally used. Rather than worrying about whether students are going to lose intelligence by using AI, perhaps the educators should spend some time trying to figure out how to help them responsibly use AI. 

By the way, this particular post was admirably assisted by AI voice recognition so that I didn't have to type this whole thing into my computer.

Using AI as an excuse or as a crutch probably has the same effect as spending time with video games or social media.  But continuing to use hallucinations as an excuse for failure to learn about or utilize AI will have the same effect as these other computer interactions.  Using AI with Google Gemini, NotebookLM, and Google Gems can effectively reduce hallucinations to almost nonexistence. Additionally, utilizing carefully drafted prompts can substantially increase AI's accuracy. 

MyHeritage Scribe AI: Genealogy Tool

 

Scribe AI 

Deciphering the remnants of the past has long been a task of patience and specialized skill for family historians. Whether it is a faded letter in 19th-century cursive, a gravestone weathered by a century of elements, or a family crest with forgotten symbolism, the barrier between the record and the researcher is often one of legibility and context. To address these challenges, MyHeritage has introduced Scribe AI, a tool designed to transcribe, translate, and interpret historical materials using generative artificial intelligence.

At its core, Scribe AI—an acronym for Scientific Record Indexing Base Engine—is built to handle the varied and often difficult materials that populate genealogical collections. Rather than a simple text-to-digital converter, the system applies specific procedures based on the type of material uploaded. For a handwritten letter, it provides a full transcription; for a historical photograph, it offers an analysis of clothing styles and visual clues to estimate a date and location.

One of the most practical applications for the active researcher is the tool’s ability to interpret complex documents. When a user uploads a record, the AI does more than just read the words; it extracts key genealogical details such as names, dates, and relationships. It also provides a section on historical context, explaining the significance of the document type or the era in which it was created. For those working with international records, the system can translate text from over 50 languages into the user’s primary language, effectively lowering the barrier for research in foreign archives.

The utility extends to physical artifacts that are notoriously difficult to document. Gravestones, for instance, are analyzed not just for their inscriptions but for the iconography and symbolism carved into the stone. Similarly, heraldic coats of arms are explained through their design elements and historical plausibility. For documents spanning multiple pages, the AI processes the entire file as a single contextual unit, ensuring that the narrative flow of a long letter or a legal document remains intact.

Integration is a key feature of this release. Scribe AI is accessible via a dedicated landing page, but it is also woven into the existing MyHeritage ecosystem. Users can apply the tool to photos and documents already stored in their "My Photos" section or use it directly while viewing records in the site’s vast historical database.

From a technical and ethical standpoint, the development of Scribe AI includes a focus on privacy. MyHeritage has stated that documents and photos processed by the tool are used solely to generate results for the user. These materials are not indexed, made searchable for others, or used to train new AI models.

For the genealogist, the arrival of such technology represents a shift in how time is spent. By reducing the hours traditionally required for manual transcription and initial interpretation, researchers can focus more on the "next steps"—a feature the AI also provides by suggesting further avenues of inquiry based on the record’s content. As family history increasingly moves into the digital and AI-assisted realm, tools like Scribe AI aim to make the insights hidden in old records accessible to a broader audience of researchers.

This video provides a visual walkthrough of the Scribe AI interface and demonstrates its transcription and interpretation capabilities in real-time.

Here is an example of a Scribe AI analysis:

The actual report was much longer.

https://youtu.be/zbbyRnBSPz8?si=ePmhW6EWtAVvciUN

MyHeritage Family Infographics Explained

MyHeritage.com 

For many family historians, the challenge of research is not just finding the data, but making sense of the patterns hidden within it. While traditional pedigree charts and group sheets are essential for organization, they often struggle to convey the broader story of a family’s migration, longevity, or social trends. MyHeritage has recently introduced a tool called "Family Infographics" that aims to bridge this gap by transforming raw genealogical data into a series of visual narratives.

The Family Infographics feature operates by scanning a user's existing family tree and synthesizing the information into a cohesive visual report. Rather than requiring manual entry, the tool pulls from the dates, locations, and relationships already established in the database. The result is a collection of charts and maps that provide a high-level overview of a family’s history, making complex data points more accessible at a glance.

The infographics cover a wide range of demographic information. Users can view statistics on life expectancy across generations, identify the most common first names in their lineage, or see the distribution of birth months among their ancestors. Geographical data is also a primary focus; the tool generates maps that illustrate where family members were born, married, and died, providing a clear visual representation of a family’s movement over time.

Beyond the statistical summaries, the feature highlights specific "milestones" and "extremes" within the tree. It identifies the oldest living relatives, the most prolific branches of the family, and couples with the longest marriages. By focusing on these specific narratives, the tool serves as an entry point for family members who may not be active researchers but are interested in the highlights of their shared heritage.

Accessibility and sharing are central to the design of this new feature. The infographics are generated automatically and are available through both the MyHeritage web platform and mobile application. Because the reports are formatted for visual clarity, they are easily shared via email or social media, allowing researchers to present their findings in a format that is more engaging for a general audience.

For the serious genealogist, these infographics offer a different perspective on their research, occasionally revealing gaps in data or unexpected trends that might be missed in a standard list format. It represents a continuing shift in the industry toward data visualization, turning the "dry" facts of the past into a more dynamic story for the present.

A New Rule for Using AI in Genealogy

Rule #4 You are the Master, AI is the Assistant

I have spent many years watching the genealogy landscape shift, from the days of cranking through microfilm in a dimly lit library to our current era of instant, global digital access. We are now standing at the threshold of the most significant transition I’ve seen in my decades of research: the rise of Artificial Intelligence. As I’ve often said, genealogy is not a hobby of collecting names; it is a discipline of verifying evidence. We now have a powerful new "assistant" in this journey, but we must be very careful to maintain the correct relationship with this technology.

In the world of computer science, there is a concept known as the "master/servant" relationship. While it might sound like something out of a Victorian novel, it describes the technical architecture of how complex tasks are distributed across a system. One central "master" process directs "servant" processes to perform specific duties. It’s remarkably similar to how a large archive operates, with a head archivist coordinating various clerks to pull records from different stacks. When you use an AI tool today, you are essentially engaging this technical workforce. However, the most important lesson for us as family historians is the functional one: in this partnership, you are the Master, and the AI is the Servant.

I see a growing number of researchers—both beginners and those who should know better—treating AI as an "oracle" that can be a friend or simply "do" their genealogy for them. But we must remember one of my Rules of Genealogy: Rule Fifteen: A fact is not a fact unless you have a record to prove it.  AI only "knows" your family through what it can find online and if it cannot find and produce the appropriate source for its information, then the information is useless.

To bridge the gap between "old school" standards and "new school" tools, we must act as "forensic auditors" of our own research. When your "servant" (the AI) brings you a lead—perhaps a transcription of a difficult-to-read land deed or a summary of a probate file—your job is to verify any reponse by examining the original records. We use AI for the "heavy lifting" of data processing, but we keep the "intellectual labor" for ourselves. We provide the judgment and the critical eye; the AI provides the processing speed.

Maintaining this relationship requires a shift in how we work. Instead of asking broad, vague questions, we must provide highly structured "master prompts" that force the AI to analyze evidence rigorously. We should ask it to highlight conflicts and cite the specific records it is using. Once the AI provides a lead, we treat it with the same skepticism we would apply to a printed family history from a hundred years ago—we verify every claim against the primary sources. By staying in the "master" seat, we can embrace these incredible advancements while ensuring our family stories are built on a foundation of solid, verified truth. The tools change, but the standards of good research remain the same. Here is a revised example, of a prompt that establishes instruction for the AI Chatbot to act as a 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.

Before finalizing any conclusion, perform a 'Red-Team' analysis: Identify the weakest link in the evidence chain and suggest one specific scenario that could disprove the current hypothesis.

If a citation cannot be fully formed due to missing data in the record, use placeholders (e.g., [publisher unknown]) and flag the missing element as a research task.

When analyzing records from before 1800, account for archaic spelling, secretary hand transcription errors, and legal Latin terminology relevant to the jurisdiction.

Here is a recent video I did that adds some additional guidance for AI's application to genealogy and genealogical research. 

Developing an Ethical and Safe Use of AI for Genealogy - James Tanner

This post was written with the assistance of Google Gemini 3.1 Pro. 

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.