Inside the Black Box: How LLMs Process Text

Article AI LLM NLP March 15, 2025

Large Language Models (LLMs) have revolutionized how we interact with AI, but their inner workings remain mysterious to many. This article demystifies how text flows through these complex systems—from input to output—revealing the fascinating process of tokenization, neural processing, and text generation.

Step 1: Tokenization — Breaking Text Into Digestible Pieces

When you input a prompt like "Explain quantum computing," the LLM doesn't process it word by word. Instead, it uses a process called tokenization to break your text into smaller units called tokens.

What Are Tokens?

Tokens can be words, parts of words, or even individual characters, depending on the tokenization algorithm. For English text, a token is typically 4 characters long on average, making "quantum computing" approximately 4-5 tokens.

Input: "Explain quantum computing"

Tokens: Ex plain quantum compu ting

Step 2: Embedding — Translating Tokens to Numbers

Once tokenized, each token is converted into a numerical representation called an embedding vector. These vectors typically have hundreds or thousands of dimensions, capturing the semantic meaning of each token in a mathematical space.

Interactive Embedding Visualization

Interactive visualization of word embeddings in 3D space. Notice how semantically related words are positioned closer together, while unrelated concepts are further apart. In reality, these vectors exist in hundreds of dimensions.

While our visualization simplifies embeddings to just three dimensions (X, Y, and Z coordinates), real LLM embeddings exist in much higher-dimensional spaces—typically 768, 1024, or even 4096 dimensions. This high dimensionality allows the model to capture subtle semantic relationships that would be impossible in lower dimensions. Each dimension can represent a different abstract "concept" or feature of language, creating a rich mathematical space where semantic similarity can be measured as proximity between vectors.

For example, the token "quantum" might be represented as a vector of 768 or more numbers, where each number captures some aspect of the token's meaning, context, and relationships to other concepts.

Step 3: Processing — The Transformer Architecture at Work

The heart of modern LLMs is the transformer architecture, which processes all tokens simultaneously through multiple layers of attention mechanisms and neural networks.

Key Components of Processing
  • Self-Attention: Each token "pays attention" to all other tokens, weighing their relevance to itself
  • Feed-Forward Networks: Process the attention-weighted information
  • Residual Connections: Help maintain information flow through deep networks
  • Layer Normalization: Stabilizes the learning process

A typical modern LLM might have anywhere from 12 to 100+ layers of these components, with each layer refining the representation of the text. As information flows through these layers, the model builds an increasingly sophisticated understanding of the input.

Step 4: Generation — Predicting the Next Token

After processing the input, the LLM generates output one token at a time. For each new token:

  1. The model calculates probability scores for every possible next token in its vocabulary (which can contain 50,000+ tokens)
  2. It selects the most appropriate token based on these probabilities (with some randomness for creativity)
  3. The newly generated token is added to the input sequence
  4. The process repeats, with the model considering all previous tokens when generating each new one
Generation Example

For our "Explain quantum computing" prompt, the model might generate:

First token: "Quantum"
Second token: "computing"
Third token: "is"
Fourth token: "a"
Fifth token: "field"
And so on...

The Scale of Modern LLMs

What makes modern LLMs so powerful is their immense scale:

  • Parameters: From billions to trillions of adjustable values that encode the model's knowledge
  • Training Data: Hundreds of billions to trillions of tokens from diverse sources
  • Context Window: The ability to consider thousands to millions of tokens at once

This scale enables LLMs to capture intricate patterns in language, store vast amounts of world knowledge, and generate coherent, contextually appropriate text across countless topics.

Key Takeaway

LLMs process text through a sophisticated pipeline of tokenization, embedding, multi-layered neural processing, and probabilistic generation. While we often think of them as "thinking" or "reasoning," they're fundamentally pattern-matching systems operating on statistical principles—albeit at a scale and complexity that produces remarkably human-like outputs.

Limitations of the Token-Based Approach

Understanding how LLMs process text also helps us understand their limitations:

  • Token Blindness: Models don't "see" words as we do, leading to occasional misunderstandings of compound words or rare terms
  • Context Window Constraints: Even with large context windows, models eventually "forget" information from much earlier in the conversation
  • Statistical Nature: LLMs generate text based on statistical patterns, not true understanding, which can lead to plausible-sounding but incorrect information

Conclusion

The journey from input to output in an LLM reveals both the elegance of modern AI design and its fundamental limitations. By understanding this process, we can better appreciate these tools' capabilities while using them more effectively and responsibly.

As LLM technology continues to evolve, we're likely to see improvements in tokenization methods, context handling, and factual reliability—but the basic pipeline of tokenize, process, and generate will likely remain the foundation of text-based AI for years to come.