GPT models operate under the hood using Transformers — a powerful neural architecture designed for understanding sequences like text. Contrary to what we might think, these models don’t work directly with human-readable characters or words. Instead, there's a fascinating pipeline that transforms your prompt into a format the model can process.

🧩 Step 1: Breaking Down the Prompt

When you type a prompt, the first thing that happens is the text is broken into smaller pieces. This isn't just for fun — it helps the model:

Handle longer inputs
Understand unseen or misspelled words
Maintain flexibility with different word forms

These smaller units are called tokens, or more technically, subword units. This tokenization step is handled by a separate program called a tokenizer, which uses a variety of algorithms to break down words based on different rules and contexts.

If you're curious about how tokenizers work in detail, here’s a great resource by HuggingFace given below:

Tokenizer in Transformers

🔄 Step 2: Tokens → IDs → Embeddings

Once the text is tokenized, each token is mapped to a unique number called a token ID, based on the model’s internal vocabulary.
You can think of this vocabulary as a giant lookup table that maps each known token to a number.

These token IDs are then converted into high-dimensional vectors using something called an embedding matrix. These vectors — called embeddings — represent the tokens in a format that the neural network can understand and work with.

🧠 Step 3: From Generic to Contextual Meaning

nitially, token embeddings are context-independent — they mean the same across different sentences. For example:

S1: Bird flies high
S2: I am high

The word “high” would have the same embedding in both cases, even though the meaning clearly changes based on context.

That’s where the real magic begins.

Through self-attention and feedforward layers techniques inside the Transformer, the model begins to refine these embeddings. It learns how each token relates to others in the sentence — and updates the embeddings to reflect the true, contextual meaning.

🧬 Step 4: Deep Understanding

These refined embeddings are passed through multiple layers of the neural network, each extracting increasingly complex and abstract features. As the data flows through the layers, the model uncovers:

Sentence structure (syntax)
Emotions or tone (sentiment)
Intent, reasoning, and relationships

All of this happens in a fraction of a second — resulting in the model generating coherent and meaningful responses.

🔚 Wrapping Up

So next time you enter a prompt, remember: behind the scenes, it's not just "text in, text out."

What starts as a simple sentence turns into a rich, high-dimensional representation that lets GPT understand you with remarkable intelligence.

Stay tuned for my next blog on Positional Encoding — how Transformers keep track of word order without using recurrence.

Until then, take care and peace ✌️

🧠 How GPT Understands Your Prompt — From Text to Tokens to Meaning