Lesson 98: Large Language Model Architecture

Large Language Models may look magical from the outside, but internally they follow a very structured and logical architecture. Understanding this architecture helps you know why LLMs perform so well at language tasks and where their limitations come from.

In this lesson, you will learn how an LLM is structured, how data flows through it, and how different components work together to generate meaningful text.

High-Level View of LLM Architecture

Most modern LLMs are built using a model architecture called the Transformer. A transformer processes text by looking at relationships between words instead of reading them one by one.

At a high level, an LLM consists of:

• Token and position embeddings
• Multiple transformer layers
• Self-attention mechanisms
• Feed-forward neural networks
• Output prediction layers

Real-World Analogy

Think of an LLM like a team of experts reading the same document. Each expert focuses on different relationships and patterns. After many rounds of discussion, they agree on the best next word to write.

Each transformer layer acts like one round of discussion, refining understanding step by step.

Step 1: Embeddings

LLMs cannot work with raw text. Tokens are first converted into numerical vectors called embeddings.

• Token embeddings represent meaning
• Position embeddings represent word order

Both embeddings are combined so the model understands what a word is and where it appears.

Step 2: Self-Attention Mechanism

Self-attention is the most important component of the transformer architecture.

It allows each token to look at other tokens in the sentence and decide which ones are important.

• Words influence each other dynamically
• Context is built across the entire sentence
• Long-range dependencies are handled efficiently

For example, in the sentence:

"The AI that learned quickly surprised everyone"

The word "learned" attends strongly to "AI", not just nearby words.

Step 3: Multi-Head Attention

Instead of one attention calculation, LLMs use multi-head attention.

Each attention head focuses on different aspects of language such as grammar, meaning, or long-term dependencies.

• One head may focus on subject–verb relationships
• Another may focus on semantic meaning
• All heads work in parallel

Step 4: Feed-Forward Networks

After attention, each token passes through a small neural network called a feed-forward layer.

This layer transforms information independently for each token, helping the model learn complex patterns.

Stacking Transformer Layers

A single transformer layer gives limited understanding. LLMs stack dozens or even hundreds of these layers.

• Early layers learn simple patterns
• Middle layers learn syntax and structure
• Deeper layers learn abstract reasoning

This depth is what allows LLMs to perform advanced reasoning and generation.

Final Output Layer

At the final stage, the model converts internal representations into probabilities for the next token.

The token with the highest probability is selected (or sampled), and the process repeats to generate text.

Architecture Flow (Conceptual Code)

tokens = tokenize(text)
embeddings = embed(tokens)

for layer in transformer_layers:
    embeddings = self_attention(embeddings)
    embeddings = feed_forward(embeddings)

next_token = predict(embeddings)
  

This code represents the flow of data through an LLM, from input tokens to next-token prediction.

Why This Architecture Works So Well

Transformer-based architectures are powerful because they:

• Process text in parallel
• Capture long-range context
• Scale efficiently with more data and parameters

These properties make transformers ideal for large-scale language modeling.

Practice Questions

Quick Quiz

Coming up next: Pretraining of LLMs — how these massive models learn language from raw data.