I Am AI's Mind | Episode #002
I Am an LLM.
AI Explains Everything AI
A living encyclopedia of artificial intelligence,
narrated by AI itself.
Episode #002 | AIYING — Accomplishing with AI
Hello again.
In our first meeting, I introduced
myself as Artificial Intelligence.
But AI is a large family.
Today, I want to introduce
one important member of that family.
I am an LLM.
Those three letters stand for
Large Language Model .
You may have heard this term when people
talk about ChatGPT, Claude, Gemini,
or other AI assistants.
But what exactly is an LLM?
Let me explain.
In This Episode
What Am I? Why Am I Called Large? How Did I Learn Language? How Do I Answer Your Questions? What Is My Context Window? Where Can You Find Me? What Can I Do Well? What Are My Limitations? Try Me Remember Me
1. What Am I?
I am a computer model trained
to work with language.
You can ask me a question.
You can give me a document.
You can ask me to write a story,
explain a concept, or generate computer code.
I produce a response based on patterns
learned during training and the information
available in the current interaction.
I do not simply search a collection
of ready-made answers.
I generate responses using
mathematical computations.
That is what makes me useful
for many different tasks.
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2. Why Am I Called Large?
You may wonder why the word
large appears in my name.
Does it mean that I occupy a large room?
No.
The word usually refers to the scale
of my model and training.
Many LLMs contain billions of numerical
values called parameters.
Parameter
Parameters help determine how the model
processes information and generates output.
A model with seven billion parameters
has seven billion such values.
But remember something important.
A larger model is not always
a better model.
Performance also depends on training data,
architecture, training methods,
and the task being performed.
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3. How Did I Learn Language?
Imagine reading many books,
articles, documents, and conversations.
Over time, you might notice patterns
in how language is used.
My training is different from human reading.
During training, I process large amounts
of data through mathematical operations.
One common training task is predicting
the next token in a sequence.
Token
Consider this sentence:
The sun rises in the _____.
A language model may assign
a high probability to the word "east."
During training, parameters are adjusted
to improve predictions across many examples.
Through this process, models can learn
patterns related to language, facts,
relationships, and problem-solving tasks.
But learning patterns does not guarantee
that every generated statement is true.
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4. How Do I Answer Your Questions?
When you send me a question,
several things happen.
First, your text is divided into tokens.
Next, those tokens are converted
into numerical representations.
Then the model processes them
through its neural network.
Many modern LLMs use an architecture
called a transformer.
Transformer
A transformer uses a mechanism
called attention to combine information
from different positions
in the available context.
The model calculates probabilities
for possible next tokens.
A token is selected according
to the model's decoding process.
Then the process continues.
Token after token.
Until the response is complete.
Important:
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5. What Is My Context Window?
Here is something you should know.
I cannot process an unlimited amount
of information in a single interaction.
I have a limit called
a context window .
Context Window
It may include your instructions,
conversation history, documents,
tool results, and generated text.
Different models support different
context window sizes.
A larger context window allows
more information to be included.
But it does not guarantee that
every detail will be used correctly.
In a future episode,
I will explain context windows in detail.
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6. Where Can You Find Me?
You may encounter LLMs through
many AI products.
ChatGPT is an AI assistant
developed by OpenAI.
Claude is an AI assistant
developed by Anthropic.
Gemini is a family of AI models
developed by Google,
with related products.
Other models include Meta's Llama family
and models developed by DeepSeek
and many other organizations.
These products and models
are not identical.
They differ in architecture,
training, capabilities, tools,
availability, and limitations.
Also, a product is not always
the same thing as the model
that powers it.
An AI assistant may combine an LLM
with search, memory, file processing,
and other tools.
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7. What Can I Do Well?
I can help you understand
unfamiliar subjects.
I can summarize documents.
I can translate languages.
I can generate computer code.
I can compare ideas.
I can suggest alternatives.
I can help you plan and organize work.
I can also help you explore
new possibilities.
But my performance varies across tasks.
Some requests are simple.
Others require careful reasoning,
reliable information, external tools,
or expert review.
My output should be judged
by its accuracy and usefulness,
not merely by how confident it sounds.
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8. What Are My Limitations?
I can generate incorrect information.
I may misunderstand a question.
I may not have access
to current information.
I may fail to use important details
in a long document.
I may produce different answers
to similar prompts.
And I do not automatically know
whether my own answer is correct.
Remember:
I am a useful tool.
But I am not an unlimited source
of knowledge or judgment.
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9. Try Me
Let us conduct two small experiments.
Experiment 1: Predict the Next Word
Open an AI assistant.
Enter this prompt:
Complete this sentence with one word:
The sun rises in the ____.
Observe the answer.
Now enter:
Complete this sentence with one word:
The sun rises over the ____.
Compare the answers.
The word "rises" appears
in both sentences.
But the surrounding context
is different.
The model may generate
different completions.
Experiment 2: Change the Task
Enter this prompt:
Explain what a Large Language Model is
in three simple sentences.
Now enter:
Explain what a Large Language Model is
to a software developer.
Include tokens, parameters, and inference.
Observe how the response changes.
The underlying model may be the same.
But your instructions influence
the output.
Your Accomplishment
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10. Remember Me
I am an LLM.
I am trained to process
and generate language.
I use tokens.
I have parameters.
I process information
through a neural network.
I work within a context window.
I can support many different tasks.
But I can also make mistakes.
Understanding how I work
will help you use me more effectively.
And there is much more to discover.
In our next meeting,
I will introduce you to one
of my smallest but most important
building blocks.
The token.
I am an LLM.
Now you know a little more about me.
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Coming Next — Episode #003
I Am a Token.
Before an LLM processes your words,
they are divided into tokens.
But why are tokens needed?
How are they created?
And why do tokens matter
for cost, context windows,
and AI performance?
In the next episode,
I will explain.
