# Decoding AI Jargons With Chai

I have started learning GANAI from [Chaicode.com](http://Chaicode.com). In our first lecture, we discussed the foundation of AI.

We learned about Google's 2017 article titled *“Attention Is All You Need.”*

We studied the Transformer model architecture. These are some of the topics I am learning:

1. Transformer - Model Architecture
    
2. Encoder
    
    In the encoder, when we give input to any AI model, it is known as a **prompt**. This input then goes through a process called **tokenization**, where the input language or text is converted into numbers. Each word (or subword) in the input is assigned a number based on the AI model's algorithm — such as **Byte Pair Encoding (BPE)** used in models like ChatGPT.
    
    **Example:**
    
    ```plaintext
    Input: "I love AI"
    After Tokenization (BPE): [34, 87, 56] (Each number represents a token from the vocabulary.)The cat sat on the mat
    Generate Token is 
    [976,9056,10139,402,290,2450] // example
    ```
    
3. **Decoder**
    
    When tokens are converted back into text, the AI model uses a **decoder**. The decoder takes all the tokens and generates the final output (human-readable text).
    
    ```plaintext
    Input:
    The cat sat on the mat
    
    Generated Tokens (Example):
    [976, 9056, 10139, 402, 290, 2450]
    
    Decoded Output:
    The cat sat on the mat
    ```
    
4. **Vector Embeddings:**
    

**Semantic Meaning**

**Positional Encoding** helps the AI model understand the **position of each word** in a sentence — because the meaning of a sentence depends not just on the words, but **how they are arranged**.

1. **Positional Encoding**
    
    Positional encoding like there name what position given all input that vise dicided what sentence means
    
    Example 1: Same words, different order
    
    * *The cat sat on the mat*
        
    * *The mat sat on the cat*
        
    
    These two sentences use the **same words**, but the **order is different**, so the **meaning changes completely**.  
    Without positional encoding, the model might treat them as the same.  
    But with positional encoding, the model knows:
    
    So, positional encoding ensures the model understands **who is doing what**.
    
    1. ### Example 2: Same word, different meaning
        
        * *The river bank is peaceful.*
            
        * *The RBI bank is secure.*
            
        
        Even though it’s the same word, the meaning changes depending on:
        
        * The **context** (surrounding words like “river” or “RBI”)
            
        * The **position** of those words in the sentence
            
        
        > This is how the model figures out the **correct meaning** using both **semantic value** and **position**.
        
2. **Self-Attention**
    
    All tokens talk with each other because it helps decide their meaning.
    
3. **Multi-Head Attention**
    
4. **Temporal Knowledge Cutoff**
    
    It means the AI model does not have information about what is currently happening in the world — this is called **knowledge cutoff**.
    
5. **Vocabulary Size**  
    The total number of tokens a model can recognize is called its **vocabulary size**.
    
6. **Temperature**
    
    When we use the AI model in the playground or implement it in our application, there's an option called **temperature**, with a value between 0 and 2.  
    When the temperature is high, the answers become more random and creative.  
    When the temperature is low, the answers are shorter and more focused or accurate.