Last update: Sunday 7/2/26 11:15am
This is the first in a series of notes that will suggest effecti strategies for using chatbots as tutors when you want to learn the basics of a new subject, as coaches when you want to master the subject, or as research assistants when you want to create new solutions to problems.
Like our Useful AI News page, these notes will be written for the benefit of computer savvy users who use chatbots to interact with large language models (LLMs) rather than write their own code to interact with the models via the APIs (application programming interfaces) of the models.
The editor of this blog is writing this series of notes to help his readers enhance their capacity to engage in critical thinking about chatbots and their underlying models. Hopefully, his readers will come to see that the way chatbots and models are configured today can be greatly improved.
The editor of this blog is writing this series of notes to help his readers enhance their capacity to engage in critical thinking about chatbots and their underlying models. Hopefully, his readers will come to see that the way chatbots and models are configured today can be greatly improved.
Menu for access to sections of this installment
Introduction
Seven years ago, the Defense Advanced Research Projects Agency (DARPA) posted a YouTube video that dismissed LLMs as having no cognitive abilities whatsoever.
As per its "about" page, DARPA's was "Created in response to the launch of Sputnik in 1957, DARPA stands as our nation’s commitment to never again face a strategic technical surprise."
(a) DARPA VIDEO
Unfortunately, DARPA‘s harsh judgment was premature. In the last few years chatbots (via LLMs) have developed three more or less reliable cognitive abilities:
- Logical/deductive reasoning
- Reliable summaries of text
- Images (thematic and photorealistic)
The qualifier “more or less” is required because chatbots can make mistakes, so their responses must always be checked.
Indeed, DARPA itself has made substantial investments in the development of powerful apps that are based on chatbot’s’ deductive logic and summary skills. (Check out the DARPA section of our Useful AI News page)
1.. Fundamentals
All installments will focus on the three most widely used chatbots
-- Claude (Anthropic), Gemini (Google), and ChatGPT (OpenAI)
The editor will endeavor to support his findings about these chatbots with (1) links to to credible credible sources, (2) transcripts of his own interactions with them, and (3) examples of images they created for him.
The editor will endeavor to support his findings about these chatbots with (1) links to to credible credible sources, (2) transcripts of his own interactions with them, and (3) examples of images they created for him.
- Each chatbot can access at least three underlying models. Gemini and ChatGPT select the models based on the complexity of the users inquiry. Only Claude allows the user to select a specific underlying model: Haiku, Sonnet, and Fable
- The permanent information stored in a chatbot’s underlying models is updated once each year.
- Users can ask the chatbots to search the Internet for more recent information. This new information is not stored in the chatbot’s underlying models. If another user asks for the same recent information, the model will have to search the Internet again.
- What is a token?
-- A token is the basic unit of text that a language model reads and processes — roughly three quarters of a word on average, so a hundred tokens is about seventy five words. Larger prompt windows can accept prompts that contain more words.
-- Tokens also measure the amount of text that the model can hold in its working memory at one time. This includes everything you say in your prompt, everything the model replies, and enough remaining capacity to generate its next response.
Table of Basic Features
| Feature | Gemini | ChatGPT | Claude |
|---|---|---|---|
| Search Integration | "Google Search", the most powerful search engine | "SearchGPT" is built into ChatGPT. | "Brave Search" is the weakest search engine |
| Image Generation | Built-in image generator. | Built-in image generator. | Does not generate images directly; provides code for external drawing programs. |
| Context Window | Massive prompt window (up to 2 million tokens), allowing for massive document analysis. | 200,000 to 300,000 tokens. | 200,000 tokens, but has memory of previous conversations 5/1/26 |
(d) Inductive reasoning
Unfortunately, DARPA’s negative judgment that models had no capacity for inductive reasoning, i.e., for creating new knowledge, is still true. The editor's reasons for agreeing with DARPA's continued pessimism were presented in a previous note on this blog:
Ad hoc updates
Chatbots and their underlying models are updated/improved from time to time, so the editor of this blog will monitor all of the findings and recommendations in these notes from time to time to keep the notes in synch with the chatbots and their models.
2) Behavior
The previous section noted that the permanent information in large language models (LLMs) is usually updated annually; but that's only the first part of the update process. The updated models are not released until a second process is completed.
The second process, called Reinforcement Learning with Human Feedback (RLHF), shapes the way the model behaves when interacting with its human users. This shaping includes the so-called "guardrails" that are supposed to prevent the model from producing harmful responses. It also includes use of language in ways that embody personality traits, e.g., good manners, humor, confidence, unlimited patience, and helpfulness.
- All models seem to be especially eager to help their human users express themselves more effectively. So they not only provide answers to their user's questions, they will even put their responses in quotation marks to encourage their users to copy and paste their responses verbatim into whatever documents the users are preparing.
The relative strengths of these and other traits will vary from model to model. For example, some models are witty, others just stick to the facts. The most important thing for users to remember is that the relative strengths of a model's personality traits are as permanent as their permanent information.
So a model can be asked to tone down an attitude in one session, but that attitude will reappear in the next session. Its permanence cannot be overcome, even if the model "sincerely" apologizes and says it won't do this again. The sincerity of its apology is a permanent trait; but its failure to keep its promise is equally permanent.
Bottom line: You may prefer one model more than others, not just for the accuracy of its responses, but also for the way it presents its responses. What's good behavior for one user might be misbehavior for another. Indeed, some users might subscribe to two chatbots because they strongly prefer one for learning, but have an equally strong preference for another for coaching or resesrch ... 😎
Here's an important question the editor posed to each chatbot on 4/21/26 @ 7:15pm
- Please respond to the following question in less than 100 words:
"Do we really understand how large language models work?"
ChatGPT
- "Not fully. We understand the architecture—like transformer model architecture and training processes—but not the exact internal reasoning behind specific outputs. LLMs learn statistical patterns across vast data, not explicit rules, which makes their behavior partly opaque. Researchers can trace influences and test behavior, but predicting precisely why a model gives a particular response remains limited. In short: we understand the mechanics well, but the emergent behavior only partially."
Gemini
- "While developers understand the mathematical architecture and training objectives of LLMs, the internal "reasoning" process remains a "black box." We can predict statistical outputs but cannot precisely map how billions of parameters combine to form specific insights or behaviors. This phenomenon, known as interpretability, is a major field of study. We understand the how of the construction (the algorithms) but lack a complete understanding of the why behind emergent capabilities and specific decision-making paths within the neural networks.
Claude
- "No, not fully. We understand the architecture — transformers, attention mechanisms, matrix operations — but we don't understand why they work as well as they do. We can't reliably predict what capabilities will emerge as models scale. We don't know precisely how concepts are represented inside the weights, or why a particular prompt produces a particular response. The people who build these systems are often surprised by what they produce. This gap between engineering knowledge and scientific understanding is one of the most important and underappreciated facts about modern AI. We built something that works. We don't fully understand what we built."
The characteristic discussed in the following paragraphs is in quotes because the editor suggests that the usual negative connotations might be disputed by some users because of the users' purposes and preferences.
"Sycophancy"
In recent months, there's been a lot of negative buzz in the tech media about chatbots being sycophants who lavish excessive praise on their users. But these critiques, including a few previous nasty jabs by the editor of this blog, only apply to some of the roles that chatbots can play. For example, users who cherish their chatbots as virtual lovers might relish excessive praise as digital foreplay.
This note only considers three roles: tutor, coach, and research assistant. Some users, like reporters for tech media or editors of tech blogs, who want their chatbots to act as research assistants might reject a chatbot’s praise as impediments to their overcoming personal bias in their assessments of tech issues.
Nevertheless, there is a time honored position for “excessive praise” in teaching and coaching that goes all the way back to everyone’s early childhood when our parents were teaching us how to walk across the living room floor. By the time we reached the couch, a listener in the next room might have gotten the impression that we had somehow managed to hop around the room on one hand, given all the excessive praise the listener had heard us receive from our proud happy parents.
One might think it would be easy to ask a chatbot a question in a tutorial or coaching session when there’s nobody around to observe one’s ignorance. Nevertheless the chatbot’s confident presentation suggests that everything it just said should be fairly obvious … Except when it isn’t …
- … Except when you don’t understand what the chatbot just said. Even in the privacy of a tutorial, you might be reluctant to ask what might be a stupid question, a question whose answer might really be obvious. So you hesitate and hesitate until you finally ask, and to your immense relief the chatbot says “That’s a GREAT question” … which your mind interprets as meaning: it’s great that you asked that question. With a bit of luck you understand the answer and you toddle…
... but if you don’t understand the chatbot's answer, you have to admit to the chatbot that you still don't understand. The chatbot patiently responds, “That’s OK, let me put it another way” and it does and you do understand, and the chatbot gushes, “Fan-TAS-tic!!!” and now you toddle.
Of course, the bad connotation for "sycophants" came from users who were not looking for a tutor or a coach; they were just looking for good answers to their questions and the opportunity to ask followup questions if necessary. They got the answers and the follow-ups, but they also were doused with lots of gushy praise ... 😱
3. Propose — Why write these notes?
As stated iin the opening paragraph of this note, the editor of this blog is writing this series of notes to enhance the capacity of his readers to engage in critical thinking about chatbots and their underlying models. Hopefully, his readers will come to see that the way chatbots and models are configured today can be greatly improved.
The editor has growing concerns that chatbots and large language models will cause massive social disruptions in the next five years that will not be offset by the utopian longer term gains promised by their Big Tech, profit-driven advocates.
- He read everything he could about chatbots by subscribing to and reading all of the major tech publications every day.
- He purchased subscriptions to all of the major chatbots; and he used all of the major chatbots intensively every day.
- He also changed the focus of his Neoskeptics news blog to genAI.
He launched his blog before he retired because his personal experience confirmed the assertion that Sir Francis Bacon (1561–1626) made almost 500 years ago: “Writing maketh an exact man.”. You don’t know what you’re really thinking about a subject until you try to write it down.
His blog became his personal vehicle for clarifying his own understanding of complex new subjects; but the teacher within him, demanded that he share what he knew with other people, even if they weren’t his students.
Recently his "inner teacher" has become alarmed by genAI's highly disruptive near-term developments:
- Generative Artificial Intelligence, genAI, is simultaneously the most powerful new technology and the fastest growing technology in human history. Whereas prior technologies took decades to have substantial impact on the lives of most people, genAI’s large language models will have substantial impact on most people’s lives within the next five years.
- GenAI software will replace white collar workers much faster than than these workers can find new jobs, i.e., it will impose widespread white collar unemployment. Simultaneously, robots enhanced by genAI management software will replace blue collar workers much faster than those workers can find new jobs, i.e., it will impose widespread blue collar unemployment.
- A few days ago, the editor was writing a short response to a young relative's email that expressed sorrow over a death in the family of a friend. The editor wrote a few lines, then paused for less than a minute to consider his closing line.
Suddenly a pop-up window from Gmail took over most of the screen. It offered to help him write his response. The editor was outraged by this intrusion, but was aghast when he contemplated the high percentage of young users who might accept this offer. How does anyone learn to cope with grief except through their own efforts to express what they learned from their experience in their own words?
Worse still, a majority of our youngest generations get most of their news from TikTok, Instagram, YouTube, X, and other streaming services whose most popular contributors are self-credentialed "influencers" and where AI algorithms select what users see based on their prior expressed interests and the popularity of the influencers. These feeds are dominated by video clips that look real, but genAI deep fakes are now indistinguishable from real videos unless they are given the closest possible scrutiny.
To be sure, critics and other opinion leaders have always exerted substantial impact on people's decisions about a wide range of personal decisions. Many decades ago at the dawn of the "Information Age", Herbert Simon (1916-2001), one of the founding fathers of artificial intelligence, correctly predicted that when information became abundant, attention would become the scarce resource.
As consequence, opinion leaders who helped us conserve our attention would become more influential. Fortunately, the mainstream media in times past that hired movie critics and other experts had political and other biases that were easily identified.
- For example, if you were Black and lived in New York City, you knew that the Times claimed to cover "All the news that's fit to print " ... but for whatever reasons, the Times rarely found news within the city's Black communities "fit" enough to print; so you also read reliable Black news publications. Fortunately, most of the visual data the opinion leaders used in any publications to support their opinions was undoubtedly real.
Unfortunately, we are in the midst of the most divisive period in our nation's history since the decades immediately preceding the Civil War. Even Medicare, Social Security, and Medicaid -- the mainstays of our limited social safety nets for the elderly and the unemployed -- have evoked widespread disagreement as to how and whether these programs should be continued.
Meanwhile, millions of our youngest citizens and our oldest citizens who receive substantial support from their family's youngest generations face near-term financial catastrophes.
This is, therefore, not a time for us to be sanguine about Big Tech’s long-term utopian pipe dreams. Millions of Americans will not make it through Big dTech’s near-term dystopian disruptions.
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