How ChatGPT and Transformers Work: Tokenization, Self-Attention, and Neural Embeddings (ELIF8 Guide)

When you open ChatGPT and ask it to write a Python microservice, summarize a 50-page legal contract, or compose a Shakespearean sonnet about quantum physics, the response streams back across your screen word-by-word with seemingly human-like intelligence.

Yet, deep inside the server clusters powering these systems, there is no conscious mind or biological brain.

Instead, the model is executing trillions of floating-point matrix multiplications, calculating mathematical probabilities to answer one fundamental question: "Given the sequence of words written so far, what is the single most statistically probable next word?"

Here is a step-by-step ELIF8 (Explain Like I'm in 8th Grade) guide to how Transformer neural networks operate.

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1. Step 1: Tokenization (Converting Words into Numbers)

Computers cannot directly read English letters or sentences; they can only perform arithmetic on numbers.

Before a sentence enters the neural network, it passes through a Tokenizer (like Byte-Pair Encoding / tiktoken):

Raw User Text:
"Learntrix makes science and coding simple."

Tokenized Number Sequence:
[ 48291,  3182,  7419,  323,  21948,  4210,  13 ]

On average, 1 token is roughly equivalent to 4 characters or 0.75 English words. Common words (like "the", "coding", "bank") are assigned a single unique integer ID, while rare words or code snippets are broken down into sub-word chunks.

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2. Step 2: Word Embeddings (Words as High-Dimensional Coordinates)

If we simply fed integers ($1, 2, 3...$) into the network, the computer would mistakenly assume that word #100 is "mathematically double" word #50.

To solve this, the model maps every token ID to an Embedding Vector—a list of 4,096 to 12,288 floating-point numbers that position the concept in high-dimensional semantic space:

[ Geometric Relationship in Word Embedding Space ]

Vector("King")   - Vector("Man") + Vector("Woman") ≈ Vector("Queen")
Vector("Paris")  - Vector("France") + Vector("Japan") ≈ Vector("Tokyo")

In this geometric coordinate space, words with related meanings (like "doctor", "hospital", "stethoscope") cluster tightly together, allowing the neural network to manipulate nuanced semantic relationships through linear algebra.

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3. Step 3: Positional Encodings (Understanding Word Order)

Unlike legacy Recurrent Neural Networks (RNNs) that processed text sequentially one word at a time, Transformers process an entire 100,000-token prompt all at once in parallel on GPUs.

However, word order is critical:

• "The dog bit the man." (Everyday news)
• "The man bit the dog." (Sensational news)

To preserve word order without slowing down parallel compute, Transformers add Positional Encodings (RoPE / Rotary Position Embeddings) directly onto the embedding vectors, injecting mathematical wave coordinates that tell the model the exact relative distance between every word.

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4. Step 4: The Self-Attention Mechanism (The Secret Sauce)

In 2017, Google researchers published the historic paper "Attention Is All You Need", introducing the Self-Attention Mechanism.

Consider this sentence:

"The animal didn't cross the street because it was too tired."

What does the word "it" refer to? As humans, we know "it" refers to the animal. But if the sentence ended with "because it was too wide", "it" would refer to the street.

Self-Attention allows every word to look at every other word in the sentence simultaneously and calculate a dynamic Attention Score:

                              [ The Attention Mechanism Formula ]

                                 ┌                        ┐
                                 │     Q · Kᵀ             │
                Attention(Q, K, V) = Softmax │ ────────── │ · V
                                 │     √d_k               │
                                 └                        ┘

1. Query (Q): What is this word looking for? ("I am 'it', I am looking for a subject noun").
2. Key (K): What identity does each word advertise? ("I am 'animal', I am a living noun").
3. Value (V): The actual semantic content passed forward when a Query matches a Key.

By multiplying the Query and Key matrices and passing them through a Softmax normalization, the model dynamically assigns heavy mathematical attention weight connecting "it" to "animal", resolving context flawlessly across entire books.

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5. Step 5: Autoregressive Next-Token Sampling

Once the text passes through 32 to 96 stacked Transformer layers, the model outputs a probability distribution across its entire 100,000+ token vocabulary:

[ Statistical Probability for Next Token ]
├── "system"       ──► 74.2%  (Chosen!)
├── "method"       ──► 18.1%
├── "architecture" ──► 5.3%
└── "banana"       ──► 0.000001%

The model selects the top token, appends it to the end of the prompt, and feeds the entire expanded text back into the network to predict the next token—a loop that repeats at 60 tokens per second until an <|endoftext|> token is emitted.

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6. Step 6: Alignment (RLHF and DPO)

Raw base models trained purely on internet text are unconstrained autocompletes: if you type "How do I fix my car engine?", a raw model might simply autocomplete with another question like "How do I fix my car brakes?"

To turn the base model into a helpful assistant, it undergoes:

1. Supervised Fine-Tuning (SFT): Human subject matter experts write tens of thousands of exemplary Question-and-Answer pairs.
2. Reinforcement Learning from Human Feedback (RLHF): Humans rate competing model outputs, and a reward model mathematically trains the AI to prioritize accuracy, honesty, and safety while refusing harmful requests.

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