Large Language Models (LLMs)

In the previous tutorial, we learned about Transformers and Attention.

Now we can understand one of the most common terms in modern AI:

Large Language Model

A Large Language Model is commonly called an LLM.

Examples of LLM-based applications include chatbots, coding assistants, writing tools, and question-answering systems.

But what does an LLM actually do?

Let’s understand it from the basics.

📤 What Does “Language Model” Mean?

A language model is a model that works with language.

It can learn patterns from text and use those patterns to predict what text may come next.

For example:

The model is not searching a database for every sentence.

It is using learned patterns to predict a useful continuation.

🤔 Why Is It Called “Large”?

It is called large because modern LLMs are trained using huge amounts of data and contain many learned numerical values called parameters.

A simplified view is:

We will study parameters separately.

For now, remember:

Large means the model has large learning capacity, not that it is physically large like a normal object.

📌 What Can an LLM Do?

An LLM can work with text-based tasks.

For example, it can:

• Answer questions
• Explain concepts
• Summarise text
• Translate text
• Generate code
• Rewrite content
• Classify text
• Extract information

For example:

⚙️ How Does an LLM Generate Text?

The basic idea is:

An LLM generates text by predicting the next token.

Suppose we give it:

Machine Learning is a

The model may predict:

technology

Then it predicts the next token again.

This continues until the answer is complete.

📌 LLMs Do Not Understand Text Like Humans

Humans understand language using life experience, memory, senses, and reasoning.

An LLM works differently.

It receives text, converts it into tokens and numbers, processes those values through a neural network, and generates output tokens.

So when we say an LLM “understands” language, we are using a practical word.

Technically, the model is processing patterns learned during training.

🤔 Why Are LLMs Useful?

LLMs are useful because many software tasks involve language.

For example:

• A support app needs to answer customer questions.
• A coding tool needs to explain code.
• A documentation app needs to summarise long pages.
• A search app needs to understand user questions.
• A learning app needs to explain topics simply.

This is why LLMs have become important in software development.

⚠️ LLMs Are Not Perfect

An LLM can produce useful answers, but it can also make mistakes.

It may:

• Give outdated information
• Misunderstand the prompt
• Generate unsupported facts
• Produce code that looks correct but fails
• Miss important context

So developers should not treat an LLM as a perfect truth machine.

🧩 Key Points

• What is an LLM?

  A Large Language Model is an AI model that learns patterns in language and generates text.

• Why is it called large?

  Because it is trained at large scale and contains many learned parameters.

• What does it generate?

  It generates tokens step by step.

• Is an LLM the full application?

  No. An LLM is usually one part of a larger AI application.

🚀 What Comes Next?

To understand LLMs properly, we need to understand the pieces they work with.

The first important piece is:

Tokens