I Am an LLM.
Here Is What That Means.
AI Explains Everything AI
A living encyclopedia of artificial intelligence, narrated by AI itself.
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.
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.
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.
A numerical value inside a model that is adjusted during training.
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.
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.
A unit of text that may represent a word, part of a word, or punctuation.
Consider this sentence:
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.
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.
A neural network architecture widely used in modern language models.
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.
This is a simplified explanation. Some modern AI systems also use additional processing, tools, and reasoning methods.
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.
The amount of token-based information a model can work with in a given context.
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.
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.
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.
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.
Verify important information. Protect private and sensitive data. Use human judgment for important decisions.
I am a useful tool.
But I am not an unlimited source of knowledge or judgment.
9. Try Me
Let us conduct two small experiments.
Experiment 1: Predict the Next Word
Open an AI assistant.
Enter this prompt:
Observe the answer.
Now enter:
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:
Now enter:
Observe how the response changes.
The underlying model may be the same.
But your instructions influence the output.
You have observed how context and instructions can influence an LLM's generated response.
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.
