What Happens When a Sentence Passes Through a Large Language Model?

Large Language Models (LLMs) like GPT, Claude, and LLaMA power today’s intelligent applications — from chatbots to coding copilots. But have you ever wondered what actually happens inside the model when you type a sentence? Let’s break it down step by step.

1. Input Processing: Turning Words into Numbers

LLMs don’t “see” language the way humans do. Instead, they break your input sentence into tokens — small units that might represent words, sub-words, or characters depending on the tokenizer.

For example, the sentence:

“Transformers changed AI forever.”

might become tokens like:[Transform, ers, changed, AI, forever, .]

Each token is then converted into a vector of numbers (called an embedding), representing its meaning in a high-dimensional space.

2. Positional Encoding: Remembering Order

Unlike humans, neural networks don’t inherently know word order. That’s where positional encoding comes in. Extra information is added to embeddings so the model knows that “AI changed” is different from “changed AI.”

This ensures that the model captures both meaning and sequence.

3. Transformer Magic: Attention Layers

Now the real work begins. The sentence flows through the Transformer architecture — a stack of layers designed to model relationships between words.

• Self-Attention: Each word looks at every other word and asks, “How relevant are you to me?” For instance, “changed” pays close attention to “AI” and “forever.”

• Feed-Forward Networks: After attention, the model refines each token’s representation with dense neural layers.

• Stacking Layers: Multiple layers of attention + feed-forward refine the meaning further, letting the model capture both local context (“changed AI”) and global context (“Transformers changed AI forever”).

This is why Transformers excel at capturing long-range dependencies in text.

4. Hidden Representations: Building Understanding

After passing through several layers, each token embedding carries contextual meaning. At this stage, “AI” isn’t just the word “AI” — it’s “AI in the context of being changed by Transformers.”

This contextual encoding is what gives LLMs their power to interpret nuance, metaphor, or domain-specific phrasing.

5. Output Prediction: Next-Token Generation

Finally, the model uses these contextual embeddings to predict the next token in the sequence.

• It applies a softmax function over its vocabulary (often 50,000+ tokens).

• The token with the highest probability is chosen (or sampled), and the process repeats until the model finishes generating text.

So if you asked:

“Transformers changed AI …”

the model might predict tokens like “forever,” “research,” or “development,” depending on the context.

6. From Numbers Back to Language

The predicted tokens are then decoded back into human-readable text. What you see as a fluent answer is the product of thousands of parallel computations happening behind the scenes.

Final Thoughts

When you pass a sentence into an LLM, it goes through a remarkable pipeline:

1. Break into tokens.

2. Convert tokens to vectors.

3. Encode position.

4. Apply attention and deep layers.

5. Predict the next token.

6. Decode back to text.

At its heart, the model isn’t “thinking” — it’s applying sophisticated math to capture patterns in language. Yet these steps allow LLMs to generate outputs that often feel surprisingly intelligent.