Introduction
Large Language Models (LLMs) can generate text, answer questions, summarize documents, translate languages, and assist with many other tasks.
But what actually happens inside an LLM when you send it a question?
The answer lies in its architecture.
LLM architecture describes the different components and computational layers that work together to process input text and generate an output.
A simplified view is:
User Input ↓ Tokenization ↓ Embeddings ↓ Transformer Layers ↓ Attention ↓ Feed-Forward Processing ↓ Output Probabilities ↓ Generated Text
Understanding this architecture makes it much easier to understand how modern AI language systems work.
What Is LLM Architecture?
Definition
LLM architecture is the structure of neural-network components that enables a large language model to process tokens, understand relationships between them, and generate predictions.
Modern LLMs are generally built around the Transformer architecture.
The Transformer provides the basic computational structure used by many powerful language models.
From Transformers to Modern LLMs
The development of modern language models can be simplified as:
Neural Networks ↓ Sequence Models ↓ Transformers ↓ Large Language Models ↓ Generative AI Applications
Transformers made it possible to process relationships between different parts of a sequence more effectively than many earlier approaches.
This architecture became the foundation for many modern language models.
Transformer Architecture
The Transformer architecture was introduced in the research paper "Attention Is All You Need."
Its key innovation was the extensive use of attention mechanisms to process relationships between elements in a sequence.
For language models, this means the model can evaluate how different tokens relate to one another.
Basic Transformer Structure
A simplified Transformer contains components such as:
- Input embeddings
- Positional information
- Attention mechanisms
- Feed-forward networks
- Normalization layers
- Residual connections
These components are repeated across multiple Transformer blocks.
Encoder vs Decoder Architecture
One important concept in Transformer architecture is the distinction between:
- Encoder
- Decoder
What Is an Encoder?
An encoder processes an input sequence and creates representations that capture information about that input.
A simplified structure:
Input Tokens ↓ Embedding ↓ Encoder Layers ↓ Contextual Representation
Encoder-based architectures are particularly useful for understanding or representing input information.
What Is a Decoder?
A decoder generates an output sequence.
For language generation, the decoder processes previous tokens and predicts what should come next.
Simplified:
Previous Tokens ↓ Decoder Layers ↓ Next Token Prediction ↓ Generated Text
Encoder vs Decoder
| Encoder | Decoder |
|---|---|
| Processes input representations | Generates output sequences |
| Useful for understanding input | Useful for text generation |
| Reads input to create representations | Predicts subsequent tokens |
| Used in encoder-focused architectures | Used in many generative LLMs |
Modern LLM architectures can be encoder-only, decoder-only, or encoder-decoder depending on their purpose.
GPT Architecture
GPT-style models are examples of decoder-only Transformer architectures.
GPT stands for:
Generative Pre-trained Transformer
The basic idea is:
Text Input ↓ Tokenization ↓ Token Embeddings ↓ Transformer Decoder Blocks ↓ Output Representation ↓ Next Token Prediction
The model repeatedly predicts the next token to generate a sequence.
Why GPT Uses a Decoder Architecture
A decoder-only architecture is well suited to autoregressive text generation.
The model receives previous tokens and predicts the next one.
For example:
"The future of AI is" ↓ "changing" ↓ "rapidly"
Each newly generated token becomes part of the context used to predict the next token.
Embedding Layer
Before an LLM can process text, tokens need to be converted into numerical representations.
This happens through embeddings.
What Are Embeddings?
Embeddings are numerical representations of tokens that allow neural networks to process relationships between pieces of information.
A simplified process:
Text ↓ Tokens ↓ Token IDs ↓ Embeddings ↓ Transformer
Why Embeddings Are Important
Words or tokens that appear in related contexts can develop related numerical representations.
This allows the model to work with relationships between different pieces of language.
Embeddings are therefore an important starting point for processing language inside an LLM.
Positional Information
Language is sequential.
Consider:
"Dog bites man."
and:
"Man bites dog."
The same words appear, but their order changes the meaning.
A Transformer therefore needs information about token positions.
Positional Encoding and Positional Representations
Transformers use mechanisms that provide information about the position of tokens within a sequence.
A simplified representation:
Token + Position Information ↓ Transformer Input
The exact positional mechanism can vary between model architectures.
Some Transformer models use traditional positional encodings, while many modern LLMs use other positional representation techniques.
Attention Mechanism
Attention is one of the most important components of Transformer-based LLMs.
What Does Attention Do?
Attention allows the model to calculate relationships between tokens and determine which parts of the input are relevant to one another.
Example:
"The scientist published the paper because she wanted to share the results."
To interpret the sentence, the model needs to connect:
"she" → "scientist"
Attention mechanisms help the model represent such relationships.
Self-Attention
Definition
Self-attention allows tokens within the same sequence to interact with one another so the model can calculate contextual relationships.
A simplified process:
Input Tokens ↓ Compare Token Relationships ↓ Calculate Attention ↓ Create Contextual Representations
This allows the model to process each token in the context of other tokens.
Query, Key and Value
Self-attention commonly uses three mathematical representations:
- Query
- Key
- Value
A simplified concept is:
Query → What information am I looking for? Key → What information does another token represent? Value → What information should be passed forward?
The attention mechanism calculates relationships between these representations.
Multi-Head Attention
Transformers can use multiple attention heads.
Instead of looking at relationships in only one way, different heads can process different patterns.
Conceptually:
Input ↓ Head 1 → Relationship Pattern A Head 2 → Relationship Pattern B Head 3 → Relationship Pattern C Head 4 → Relationship Pattern D ↓ Combined Representation
This allows the model to capture different types of relationships within the same sequence.
Transformer Blocks
A modern LLM contains many Transformer blocks.
A simplified block can contain:
Input ↓ Attention ↓ Residual Connection ↓ Normalization ↓ Feed-Forward Network ↓ Residual Connection ↓ Normalization ↓ Output
These blocks are repeated many times.
Each layer transforms the representations passed through the network.
Why Multiple Transformer Layers?
One Transformer layer provides one stage of processing.
Multiple layers allow the model to build increasingly complex representations.
A simplified idea:
Layer 1 ↓ Basic Patterns Layer 2 ↓ More Complex Relationships Layer 3 ↓ Contextual Patterns ... Layer N ↓ Rich Representation
The actual behavior of individual layers is much more complex than this simplified illustration.
How the Architecture Produces Text
Once the input has passed through the Transformer layers, the model produces information that can be converted into probabilities for possible next tokens.
Simplified:
Input Tokens ↓ Embeddings ↓ Transformer Layers ↓ Output Representation ↓ Token Probabilities ↓ Next Token
The process repeats until the model generates the desired response.
LLM Architecture in One Diagram
The complete simplified architecture looks like:
User Prompt ↓ Tokenizer ↓ Token IDs ↓ Token Embeddings ↓ Positional Information ↓ Transformer Blocks ↓ Attention + Feed-Forward Networks ↓ Final Representation ↓ Output Layer ↓ Token Probabilities ↓ Generated Response
LLM Architecture and Generative AI
LLM architecture is the technical foundation behind many generative AI applications.
LLMs can use this architecture to generate:
- Articles
- Conversations
- Code
- Summaries
- Translations
LLM Architecture and RAG
RAG systems can add external information to an LLM workflow.
A simplified RAG architecture is:
User Question ↓ Retrieve Relevant Information ↓ Context + Question ↓ LLM ↓ Generated Answer
The LLM architecture remains responsible for processing the provided context and generating the response.
LLM Architecture and AI Agents
AI agents can use LLMs as a reasoning and language-processing component.
A simplified workflow:
User Goal ↓ AI Agent ↓ LLM ↓ Planning ↓ Tool Usage ↓ Result
Key Takeaways
LLM architecture is built around several important components:
- Tokenization
- Embeddings
- Positional information
- Attention
- Transformer blocks
- Feed-forward networks
- Output prediction
Modern generative language models commonly use Transformer-based architectures to process tokens and predict subsequent tokens.
Understanding these components provides the foundation for understanding how systems such as GPT-style models generate language.