What Does “Architecture” Mean?
In AI, architecture means the design or structure of how the model works inside.
It shows how data moves, how the model learns from text, and how it produces answers.
Think of it like the brain design of the model — how it stores memory, understands meaning, and talks back to you.
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Core Parts of LLM Architecture
1.Tokenization
Before text enters the model, it is broken into tokens (small pieces like words or parts of words).
The model does not read full sentences; it reads these small tokens.
Each token is turned into a number so the computer can understand it.
Example: “Learning AI is fun” → [Learning] [AI] [is] [fun]
2.Embeddings
Each token is given a vector (a list of numbers) that represents its meaning.
This helps the model understand how words relate to each other.
Example: The words “king” and “queen” are close in meaning, so their vectors are similar.
3.Transformer Architecture
The Transformer is the heart of every modern LLM.
It was introduced by Google in 2017 and changed AI completely.
The transformer uses a mechanism called self-attention, which allows the model to focus on the most important words in a sentence — no matter where they appear.
Example: In the sentence “The cat that chased the mouse was fast,” the model knows “cat” connects to “was fast,” not “mouse.
4. Layers and Parameters
- LLMs have many layers (like layers in a cake).
- Each layer learns a deeper understanding of language.
- The parameters (numbers inside the model) are like memory cells that store knowledge.
The more parameters a model has, the more complex and capable it becomes.
Example: GPT-3 has 175 billion parameters; new 2025 models have over 1 trillion.
5.Attention Mechanism
The attention system helps the model decide which words are important when predicting the next word.
It’s like the model’s “focus.”
This allows LLMs to keep context, understand long sentences, and generate relevant answers.
Training Process
LLMs are trained on huge text datasets using machine learning.
During training, they
- Predict the next word in a sentence.
- Check if it’s right or wrong.
- Adjust internal parameters to improve next time.
This process repeats trillions of times until the model becomes very good at language.
7. Fine-Tuning and Reinforcement Learning
After basic training, the model is fine-tuned using specific data.
It can also be trained with human feedback, known as Reinforcement Learning from Human Feedback (RLHF).
This step helps the AI become:
- Safer
- More accurate
- Better at following instructions
Example: ChatGPT uses RLHF to give polite and useful answers.
Modern Improvements in 2025 LLM Architecture
The latest LLMs in 2025 include advanced features
- Multimodal Input: They can understand text, images, voice, and video together.
- Memory Systems: Models now remember previous chats for context.
- Efficient Training: New architectures use fewer resources but perform faster.
- Mixture of Experts (MoE): Only parts of the model activate when needed, saving time and energy.
- Tool Use: LLMs can call APIs, search the web, or use a calculator directly.