How large language model works: Explained conceptually.

Step 1: Training data collection

The model is trained on huge amounts of text:

• Books

• Articles

• Websites

• Code

• Conversations

It doesn't "memorize" like humans. It learns patterns in language.

Think: Learning grammar, tone, facts, and relationships between words.

Step 2: Tokenization (Breaking text into pieces)

Before learning, text is converted into smaller units called tokens.

Example:

"Chat GPT is amazing"

["Chat", "G", "PT", "is", "amazing"]

Tokens are not always words. They can be parts of words.

Step 3: Converting tokens into numbers

Computers don't understand text, so tokens are tuned into numbers using embeddings.

Each words becomes a vector (a list of numbers) representing meaning.

Example idea:

"King" and " Queen" will have similar vectors.

"Apple" (fruit) vs "Apple" (company) differ by context

Step 4: The core engine: Transformer architecture

Modern LLMs use a structure called a transformer.

The key innovation here is something called attention mechanism

What does attention do?

It helps model decide which words in the sentence are important for understanding this word.

Example: The cat sat on the mat because it was tired

It refers to "cat", not "mat"

The model learns this via attention.

Step 5: Learning through prediction

The model is trained using a simple idea

Guess the next word, compare, adjust

Example:

Input: "The sky is"

Model guess: "blue" ✅ or "green" ❌

If wrong:

• It adjust internal weights slightly

• Repeat this billion of times

The process is called

Training using gradient descent

Step 6: Building understanding (patterns, not facts)

Over time, the model learns:

• Grammar

• Facts

• Reasoning patterns

• Context understanding

But important:

• It does not "know" things like a human.

• It recognizes statistical patterns

Step 7: Inference (When you ask a question)

When you type something

Example:

Explain AI simply

The model:

• Convert inputs into tokens

• Process through transformer layers

• Predict the next token

• Adds is to the sentence

• Repeats until response is complete

It generates text one token at a time

Step 8: Fine tuning (Make it useful and safe)

After basic training, models are improved using:

b. Human feeback

Human rank response

• Good vs bad answer

• Helpful vs harmful

This is called: Reinforcement learning from human feedback (RLHF)

b. Instruction tuning

The model is trained to follow instructions better:

• Summarize this

• Explain simply

• Write an email

Step 9: Why it feels intelligent

Even though it's just predicting text, it can:

• Answer questions

• Write code

• Solve problems

• Explain concepts

Because language contains compressed knowledge of the world