When you ask a large language model a question, it responds in milliseconds—with clarity, context, and often insight. But beneath that fluid output lies something far more intricate: a network of mathematical layers, each playing a critical role in turning raw data into simulated intelligence.

These layers are not just processing units. They’re stages of transformation—where text becomes math, math becomes meaning, and meaning becomes language again.

Let’s dive into the internal structure of LLMs to understand how they generate responses that feel thoughtful—even if no real thought is taking place.

1. Neural Networks: Brains Made of Math

At the core of every LLM is a deep neural network, made up of:

Input layers: Where tokens are converted into vectors
Hidden layers: Where patterns are recognized and abstracted
Output layers: Where probabilities are turned into words

These layers contain parameters—millions or billions of adjustable weights—that determine how input signals are transformed. Training an LLM is essentially the process of tuning these weights so that the model becomes good at predicting what comes next.

2. Token Embeddings: Giving Words a Numerical Shape

Text input is first broken into tokens—units like "chat", "##GPT", or "2025". These tokens are mapped to vectors in a high-dimensional space via a token embedding matrix.

These embeddings encode semantic relationships:

Words with similar meanings have similar vectors
Complex ideas are represented as combinations of simpler ones
Contextual nuance begins to form even in the first layer

Embeddings are the LLM’s version of vocabulary—except every word is described numerically.

3. Positional Encodings: Learning Word Order

Words are not enough—sequence matters.

LLMs use positional encodings to inject information about word order into the model. These encodings are added to the token embeddings, allowing the model to know whether “The cat chased the dog” means the same as “The dog chased the cat.” (Spoiler: it doesn’t.)

This enables the model to process sequence-sensitive patterns, like grammar, causality, and narrative flow.

4. Attention Heads: The Model’s Selective Focus

The Transformer architecture—introduced in 2017—relies heavily on self-attention mechanisms, which allow the model to weigh the importance of every token in a sentence relative to every other token.

Each attention head in the model:

Focuses on a different pattern (e.g., coreference, syntax, topic tracking)
Assigns attention scores to tokens
Helps the model “pay attention” to what matters in context

The result: the model builds a nuanced, context-aware representation of language—far beyond simple word matching.

5. Layer by Layer: Abstraction and Meaning

An LLM typically has dozens or even hundreds of transformer layers. As input passes through these layers:

Lower layers detect basic patterns (spelling, grammar)
Middle layers abstract semantic meaning (intent, emotion, logic)
Higher layers integrate task-specific goals (answering, summarizing, reasoning)

Each layer passes its output to the next—allowing the model to build richer and more abstract representations of the input with every step.

This process mirrors how the human brain builds understanding: bottom-up and top-down at the same time.

6. Hidden States: The Model’s Mental Workspace

At each layer, the model maintains a hidden state—a set of vectors that represent the current interpretation of the input.

These states are not visible to the user, but they are crucial for:

Maintaining continuity across long texts
Tracking narrative arcs or logical steps
Storing temporary context during generation

In essence, the hidden states are the working memory of the model—transient, dynamic, and powerful.

7. Output Layer: Choosing the Next Word

After the final transformer layer, the model produces a probability distribution over its vocabulary: What’s the most likely next word?

From there, decoding strategies are applied:

Greedy decoding: Always pick the highest probability
Sampling: Choose randomly within a probability threshold
Beam search: Explore multiple possible sequences in parallel
Top-k / nucleus sampling: Control creativity and coherence

The selected word becomes the next token—and the process repeats until the response is complete.

8. Learning Through Backpropagation

How does the model get smart in the first place?

During training, the model makes a prediction (e.g., "The capital of France is ___") and compares it to the correct answer (“Paris”). The error is used to adjust the model’s internal weights—a process called backpropagation.

This happens billions of times across massive datasets.

Over time, the model becomes increasingly accurate—learning not just facts, but the structure of logic, storytelling, argument, and style.

9. Why Layers Matter: Scaling Complexity

The depth of an LLM—the number of layers—matters.

More layers = more abstraction
More parameters = more nuanced understanding
More compute = more sophisticated behavior

This is why scaling LLMs leads to emergent capabilities. At a certain depth and size, the model starts doing things it wasn’t explicitly trained to do—like solving math problems or translating across languages.

The layers aren’t just mathematical—they are layers of thought, structured to reflect and generate human-like understanding.

Conclusion: Thought, Engineered

The LLM is not a brain. It doesn’t feel or think. But its layered architecture gives it the capacity to simulate understanding with astonishing effectiveness.

From token to meaning, from structure to insight—every response you get is the output of thousands of operations across dozens of layers, each adding something new to the model’s evolving picture of your prompt.

LLMs aren’t magic. They’re engineering at its most ambitious: a stack of math, data, and logic built to mirror the way we think—one layer at a time.

Layers of Thought: Inside the Neural Networks That Power Language Models LLM DEVELOPMENT