AI Home › Topic Module Area › LLM Module › LLM Components — Overview

LLM Components

Architecture and Building Blocks of Large Language Models

Introduction

Large Language Models (LLMs) are complex AI systems composed of multiple interconnected components that work together to understand, process, and generate human-like text. Understanding these components is key to grasping how modern AI language models function.

LLM Architecture Overview

Input Processing Layer

Converts raw text into numerical representations that the model can process

2026-02-14
Tokenization
Breaking text into smaller units (tokens)
Embeddings
Converting tokens to numerical vectors
Positional Encoding
Adding information about token positions
Core Processing Layer

Transformer architecture that processes sequences and captures relationships

Attention Mechanism
Weighing importance of different tokens
Feed-Forward Networks
Non-linear transformations of representations
Residual Connections
Preserving information across layers
Output Generation Layer

Converting processed representations back into human-readable text

Probability Distribution
Calculating likelihood of next tokens
Sampling Methods
Selecting the next token to generate
Decoding
Converting tokens back to text

Detailed Component Breakdown

Tokenization

The process of breaking down text into smaller units called tokens, which can be words, subwords, or characters.

Key Aspects:

  • Byte Pair Encoding (BPE) algorithm
  • Vocabulary size typically 30,000-500,000 tokens
  • Handles out-of-vocabulary words
  • Balances token count and semantic meaning
  • Special tokens for model control
Embeddings

High-dimensional vector representations that capture semantic and syntactic meaning of tokens.

Key Aspects:

  • Typically 512-4096 dimensions
  • Learned during pre-training
  • Capture semantic relationships
  • Enable mathematical operations on words
  • Foundation for all downstream processing
Attention Mechanism

Allows the model to focus on different parts of the input sequence when processing each token.

Key Aspects:

  • Self-attention for intra-sequence relationships
  • Multi-head attention for different representation subspaces
  • Scaled dot-product attention computation
  • Query, Key, Value matrices
  • Enables handling of long-range dependencies
Transformer Blocks

The fundamental building blocks of LLMs, consisting of attention and feed-forward layers.

Key Aspects:

  • Multi-head self-attention layer
  • Position-wise feed-forward network
  • Layer normalization for training stability
  • Residual connections to prevent vanishing gradients
  • Stacked 12-96 times in modern LLMs
Positional Encoding

Adds information about the position of tokens in the sequence since transformers lack inherent notion of order.

Key Aspects:

  • Sinusoidal functions or learned embeddings
  • Enables understanding of word order
  • Relative positional encoding variants
  • Rotary Position Embeddings (RoPE) in modern models
  • Critical for sequence understanding
Output Head

The final layer that converts hidden representations into probability distributions over the vocabulary.

Key Aspects:

  • Linear projection to vocabulary size
  • Softmax activation for probability distribution
  • Sampling strategies (greedy, beam search, top-k)
  • Temperature parameter for creativity control
  • Generates next token probabilities

LLM Training Process

1
Pre-training

Training on massive text corpora using self-supervised learning

2
Architecture Design

Selecting model size, layers, attention heads, and parameters

3
Fine-tuning

Adapting the model to specific tasks or domains

4
Alignment

Training to follow instructions and behave helpfully (RLHF)