Author	: Bill Kochman
Publish	: Sep. 29, 2026
Update	: Sep. 29, 2026
Parts	: 03

Synopsis:



Continuing our discussion from part two, let us now turn our attention to the subject and process within the world of AI agents and LLMs which AI engineers have assigned the name of Generalization.

STEP 7: "GENERALIZATION"

-- Handling New Combinations Of Words

Another important capability of AI language models is called GENERALIZATION. Generalization means that a system can apply patterns it learned from examples to situations that are in fact different from those exact examples. Imagine that you're teaching a small child what a sentence is. You will offer the child many examples to learn from, such as the following:

"The dog runs."

"The cat sleeps."

"The boy plays."

Eventually, if he or she continues to persevere at the task, the child will learn how to create a sentence all on their own, even if nobody ever specifically taught that particular sentence to them. For example, consider the following brand new sentence which was formed by combining and expanding on two of the previous examples:

"The small boy plays in the sand while the dog happily runs right alongside of him."

Thus we see that the child combined things learned from some of their previous examples to create something entirely new. Well, as it turns out, as I explain in the five-part series "Conversations With Four AI Chatbots", when Large Language Models are first created, they are somewhat similar to that small child in that they too have to learn and grow. And as eventually occurred with that child, AI models can likewise learn to generate combinations of language which were NOT specifically stored as complete answers.

Furthermore, this is in fact one reason why an AI model can respond to unusual questions or requests which it has never even encountered word-for-word during its training period. However, there does exist one very important difference or distinction between this analogy and that human child, and that is the following: A Large Language Model does NOT learn through human experience, or consciousness, or even through human understanding. Quite to the contrary, the AI model's generalization comes from mathematical patterns which were learned during its training period.

STEP 8: TRAINING VS. GENERATING RESPONSES

-- The Classroom Vs. The Conversation

There are two very different stages that are important to distinguish here. Please carefully consider the following:

PHASE 1: TRAINING

Training is the long process during which a language model's internal numerical settings are adjusted. During the training stage, the language model processes enormous amounts of data and repeatedly makes predictions, measures its errors, and likewise adjusts its weights. The primary goal is to create an AI model that has learned useful patterns in language. A simple analogy would be the following:

TRAINING = going to school.

Imagine spending years studying books, doing exercises, taking tests, making mistakes, and gradually improving.

PHASE 2: GENERATING A RESPONSE AND INFERENCE

Once an AI language model has been properly trained, only then can people use it successfully and obtain satisfying results, or responses. In short, because of that training, when you type a question or comment into the prompt area of any chatbot, the already-trained model processes your input, and it generates an appropriate response. This stage of the game is commonly called INFERENCE. Inference simply means using the trained model to produce an output from a user's new input. A useful analogy would be the following:

TRAINING = going to school.

INFERENCE = using what you learned in school when having a conversation with someone.

However, the two processes should not be confused. When you are chatting with a normal deployed AI model, your individual conversation does not ordinarily cause the model's billions of learned weights to be immediately rewritten. Instead, your conversation supplies CONTEXT that the AI model can use while generating its current response.

Some AI systems may use conversations or other information later for separate training or improvement processes. It all depends on the service and its settings. That is different from the model instantly retraining itself during your conversation with it.

STEP 9: PUTTING IT ALL TOGETHER

-- The Complete AI Response Cycle

Let's now combine everything we have learned. When you send a message to an AI chatbot, a simplified version of the process looks like the following under the hood. As you can no doubt see, there is actually a lot going on. As we learned earlier, it is NOT simply a lot of copy and paste, as occurred with Hotline server chatbots three decades ago:

1. RECEIVE THE TEXT

The system receives the message you typed.

2. BREAK IT INTO TOKENS

The text is divided into smaller pieces called tokens.

3. CONVERT TOKENS INTO NUMBERS

The tokens are represented by Token IDs and then transformed into numerical representations called embeddings.

4. PROCESS THE INFORMATION

The neural network processes those numerical representations.

5. USE CONTEXT AND ATTENTION

The model examines relationships among the pieces of information available in the context.

6. CALCULATE PROBABILITIES

The model calculates which possible next tokens are likely to fit the context.

7. SELECT A TOKEN

The system chooses a token according to its generation process.

8. ADD THE TOKEN

The selected token becomes part of the growing response.

9. REPEAT

The model uses the expanded text as part of the information for the next prediction.

This process continues until the language model reaches an appropriate stopping point, and then it produces output on your device's screen in the form of a response. In a rather simplified form, the process looks like the following:

READ

↓

RELATE

↓

PREDICT

↓

ADD

↓

REPEAT

However, please remember that "READ" and "RELATE" are only human-friendly descriptions. The computer is not literally reading and thinking like a regular human being. It is in fact performing many mathematical operations on numerical representations of language.

AN IMPORTANT POINT ABOUT "UNDERSTANDING"

At this point in our discussion, you may reasonably ask the following question:

"Does the AI actually understand what I am saying?"

This is a surprisingly complicated question. That's because the word "understand" can actually mean different things. A human understands language through a combination of language, personal experience, perception, memory, physical interaction with the world, emotions, goals and through quite a few other things.

In contrast, as we have already seen, an AI language model processes language using mathematical representations and learned patterns. Again, as I stated near the beginning of this series, what you see on your screen when you talk with an AI chatbot is the result of pure science and very complex mathematical computations. That's why a Large Language Model can produce remarkably appropriate responses, and can often recognize subtle relationships between words and ideas.

However, again, this should not automatically be interpreted as meaning that the AI possesses human-like understanding or consciousness. When we say that an AI "understands" something in an everyday explanation, what we often really mean is that it has successfully processed the relationships and meaning conveyed by the language well enough, so that it can respond appropriately. That is a useful shorthand, but it should not be confused with saying that the machine experiences the world exactly as a human being experiences it.

A VITAL SAFETY NOTE: "HALLUCINATIONS"

One of the most important weaknesses of AI language models is that they can sometimes produce information that sounds convincing, but which in fact is incorrect. This is often called a HALLUCINATION. The word is an analogy. It does NOT mean that the computer is literally seeing or hearing things that are not there. In AI terminology, such a hallucination generally refers to generated content that is simply false, unsupported, or even fabricated even though it may sound perfectly believable. For example, an AI chatbot might do any of the following:

• invent a quotation that a person never said

• give a nonexistent book title

• provide a false historical detail

• invent a scientific reference

• misunderstand a question

• perform a calculation incorrectly

• confidently state something that simply isn't true

If you have read my five-part series entitled "Conversations With Four AI Chatbots", then you will already know that this is exactly what I was experiencing about a year ago when I first started using OpenAI's ChatGPT. That bot was driving me absolutely crazy with its inaccuracies, inventions and hallucinations; so much so, that eventually, even though it kept apologizing for the mistakes it was making -- which it continued to make anyway despite all of its apologies -- I shut down my OpenAI account and I switched to primarily using Anthropic's Claude.

So the big question is this: Why can this happen? What is the cause behind an AI chatbot hallucinating so much? It happens because the Large Language Model's fundamental job is simply to generate likely sequences of text. It is NOT automatically checking every statement against reality before it actually states something or prints it out. If an AI had to fact-check every single sentence before it actually typed it out, its responses would not be as spontaneous. Exactly how much of a pause there would be between each of its responses, I cannot really say. At any rate, this distinction is very important.

FLUENCY DOES NOT EQUAL ACCURACY.

FLUENCY means that the writing sounds natural, smooth, organized, and convincing.

ACCURACY means that the information corresponds to what is actually true.

The caveat is this: An AI chatbot can be extremely fluent in your language, while at the same time being completely wrong. For example, it might produce a beautifully written paragraph containing a false statement. The fact that a paragraph may sound confident does NOT necessarily make it true. In fact, I can tell you from my personal experiences with ChatGPT and Gemini that there were a lot of instances where they would project extreme confidence in what they were telling me, and even double down, and yet, nevertheless, they were still very wrong with the "facts" or their conclusions.

A lot of times they would guess at certain things over and over again, because they were so determined to provide me with the correct answer, or to find the right solution to a particular problem. I discuss this same issue more at length in the series "Conversations With Four AI Chatbots". I hope that you'll take the time to read it, because you will learn a lot from doing so.

But the lesson that we can learn here is rather clear. This is exactly why without exception, all important information should be independently verified against reliable external sources. This is particularly important for certain types of information, such as the following:

• medical information

• legal information

• financial information

• historical claims

• scientific claims

• quotations

• statistics

• current events

• names, dates, and specific factual details

In short, an AI chatbot can indeed be a remarkably useful tool. However, it should not automatically be treated as if it is an infallible source of truth, because it obviously is no such thing. So again, please check your facts before you regret not having done so!

THE BIG PICTURE

If all of the previous information still seems like too much for you to have to remember, then following is the simplest possible version of what really happens under the hood, and out of sight, the minute that you choose to type a prompt -- meaning a question or a comment -- into the text field of an AI chatbot.

When you type something into an AI chatbot:

YOUR WORDS

↓

are broken into

TOKENS

↓

which are represented as

NUMBERS

↓

those numbers are processed by a huge

NEURAL NETWORK

↓

using learned

WEIGHTS

↓

while taking account of

CONTEXT

and relationships calculated through

ATTENTION

↓

to calculate probabilities for

WHAT TOKEN SHOULD COME NEXT

↓

one token is selected

↓

then another

↓

then another

↓

until a complete response has been generated.

THE MOST IMPORTANT IDEAS/DEFINITIONS TO REMEMBER

1. TOKENS

Tokens are the small pieces into which text is divided so that the computer can process language.

2. TOKEN IDs

Token IDs are numbers used to identify particular tokens.

3. EMBEDDINGS

Embeddings are numerical representations that allow the model to represent relationships between tokens mathematically.

4. NEURAL NETWORK

A neural network is a huge mathematical system that processes those numerical representations.

5. WEIGHTS

Weights are learned numerical settings inside the model that strongly influence how it processes information and generates responses.

6. PARAMETERS

Parameters are learned numerical values within the model. Modern AI models can contain billions or even more of them.

7. TRAINING

Training is the process of adjusting those numerical values using enormous amounts of data so that the model becomes better at its tasks.

8. CONTEXT

Context is the surrounding information that helps determine what words and ideas mean in a particular situation.

9. ATTENTION

Attention is a mathematical mechanism that helps the model determine which pieces of information are related and relevant to one another.

10. TRANSFORMER

A transformer is a particular neural-network architecture that uses mechanisms such as attention to process relationships within sequences of information.

11. NEXT-TOKEN PREDICTION

This is the basic process of calculating which token should come next based on the information currently available.

12. INFERENCE

Inference is the process of using a trained model to generate an answer from new input.

13. GENERALIZATION

Generalization is the ability to apply learned patterns to new combinations and situations.

14. HALLUCINATION

A hallucination is an AI-generated statement or other content that is incorrect or fabricated even though it may sound convincing.

ONE FINAL ANALOGY

If you want one mental picture to help you to remember all of the previous information, imagine an enormous automated writing machine.

You put a sentence into the machine.

The machine breaks your sentence into pieces.

It converts those pieces into numbers.

It passes those numbers through an enormous network of mathematical operations whose settings were established through training.

The system examines the relationships among the pieces of information available to it.

It then calculates which piece of text would be a good one to place next.

It adds that piece.

Then it calculates the next one.

And the next.

And the next.

It continues doing this extremely rapidly until it has produced an entire response.

The response which then appears on your device's screen can be extraordinarily sophisticated.

It can explain complicated subjects.

It can write stories.

It can summarize documents.

It can translate languages.

It can help write computer programs.

It can answer questions.

It can carry on remarkably natural conversations.

But underneath all of that apparent conversational ability is an enormous mathematical system that has learned patterns from data and uses those patterns to generate new sequences of language. Understanding that basic process is the key to understanding what modern AI language models actually do. As I have said two times now, it is all pure science and very complex mathematical operations, and it also all happens at a very tremendous rate of speed. Certainly much faster than you or I can possibly ever think. Just wait a few more years from now. Our current world may be very different due to AI.

THE SIMPLEST POSSIBLE SUMMARY

AI Does Not Begin With A Pre-Written Answer.

Instead:

It breaks language into pieces.

It turns those pieces into numbers.

It processes those numbers through a huge learned mathematical system.

It examines relationships and context.

It calculates probabilities for what should come next.

It selects a token.

It repeats the process.

And, one piece at a time, it generates a response.

That is the basic mechanism behind the astonishing ability of modern AI chatbots to communicate with us in any human language. Isn't it mind-blowing?

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And that, my dear friends, is the end of this amazing lesson regarding exactly how Artificial Intelligence and our popular chatbots actually work. You only see the end result on your device's screen; but under the hood, they are just so busy, busy, busy working constantly to provide you with the info that you want or need, thus saving you piles of time.

With these thoughts, I will bring this article to a close. It is my hope that you have found it informative, enlightening, and I pray that it has been a blessing in your life as well. If you have an account with Facebook, Twitter, Tumblr or with any other social network, I would really appreciate if you'd take the time to click or tap on the corresponding link that is found on this page. Thanks so much, and may God bless you abundantly!

For additional information and further study, you may want to refer to the list of reading resources below which were either mentioned in this article, or which contain topics which are related to this article. All of these articles are likewise located on the Bill's Bible Basics web server. To read these articles, simply click or tap on any link you see below.

AI, Chatbots, Daemons and Demons

AI, Deepfakes and Humanoid Robots

Be Careful With ChatGPT and Other AI Chatbots!

Cognitive Computers, DARPA, OpenAI, AGI, Superintelligence and Elon Musk

Conversations With Four AI Chatbots

Is Elon Musk the Antichrist?

Power of the Purse and the Federal Reserve System

Robot Wars and Skynet: Is Sci-Fi Becoming Our Reality?

Rogue AIs: We've Been Warned!

Science and Technology: The Forbidden Knowledge?

The Internet: Our Final Frontier; Your Last Chance?

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Please avail yourself of other areas of the Bill's Bible Basics website. There are many treasures for you to discover.