How ChatGPT works?

How ChatGPT Works — Step by Step

🔹 A. Training Phase (Before You Ever Use It)

ChatGPT is trained in two major steps:

Step 1: Pretraining

• Input: Billions of sentences from websites, books, Wikipedia, conversations, etc.
• Goal: Predict the next word in a sentence.
• It learns grammar, facts, reasoning patterns, even some logic.

Example:
Input: "The capital of France is"
The model learns that the likely next word is “Paris”.

This is done unsupervised — it learns from raw text using transformer-based neural networks.

🧠 Neural Network Structure:

Imagine a neural network as a grid of neurons (like brain cells) with connections (weights) between them. The transformer model looks like this:

Input Sentence → [Embedding Layer] → [Transformer Blocks × N] → [Output Layer]

Each Transformer Block contains:

• Multi-head Self-Attention (focus on important words)
• Feed-forward layers
• Normalization and residual connections

Let’s visualize a simplified diagram:

[Input Text]
   ↓
[Tokenizer]
   ↓
[Word Embeddings]
   ↓
[Transformer Layers] -- (Self-Attention, FFN)
   ↓
[Output Probabilities]
   ↓
[Next Word Prediction]

The Self-Attention mechanism helps the model understand context, e.g., in:
"He fed the dog because it was hungry", it figures out that "it" refers to "dog".

Step 2: Fine-Tuning (ChatGPT Phase)

OpenAI fine-tunes the base GPT model using human feedback:

• People ask questions.
• They rate the answers.
• The model is reinforced to give more helpful, safe, and correct responses.

This is called Reinforcement Learning from Human Feedback (RLHF).

🔹 B. Inference Phase (When You Chat With It)

When you type:

“Write a poem about mountains”

Here’s what happens:

1. Tokenization
   Breaks your input into tokens (words/pieces of words).
2. Context Understanding
   It processes your tokens using Transformer blocks with self-attention to understand the structure.
3. Prediction Loop
   It predicts the next token, then the next, until it finishes the output.
4. Output Generation
   It stitches tokens together to form readable text.

💡 Real Example: You ask —

"What is the capital of Italy?"

1. Input Tokens:
   • "What", "is", "the", "capital", "of", "Italy", "?" ( use this platform to find number of token in your input --> https://tiktokenizer.vercel.app/?model=gpt-4
2. Self-Attention:
   • The model sees "Italy" and learns it’s the focus.
3. Prediction:
   • It predicts next token to be "Rome".
4. Output:
   • "The capital of Italy is Rome."

🔍 How ChatGPT Reaches a Conclusion

1. Pattern Matching
   It has seen thousands of sentences like:
   "Capital of X is Y" → learns the pattern.
2. Context Awareness
   It pays attention to all parts of the sentence using self-attention.
3. Probabilistic Output
   It doesn't memorize, it generates based on probability of correct answer.
4. No Real-Time Web Access (unless connected to tools):
   It knows things only up to training cut-off (e.g., Jan 2025 knowledge).

🔁 Summary