How LLMs Process Language

Large Language Models (LLMs) process language through a structured series of steps before generating responses.

Breaking Down Language – Tokenization, Embeddings, and Context Windows

Tokenization

Instead of reading full words or sentences, LLMs break down text into smaller units called tokens. These can be whole words, subwords, or even individual characters.
For example, “Artificial Intelligence” might be split into:

• Whole words → [“Artificial”, “Intelligence”]
• Subwords → [“Arti”, “ficial”, “Intel”, “ligence”]

LLMs don’t “see” language as we do—they process these tokens mathematically.

Embeddings

Once tokenized, the model converts tokens into numerical representations called embeddings. These numbers capture relationships, context, and meaning.

• Words with similar meanings have embeddings that are close together in this space.
• This allows the AI to recognize synonyms and make context-aware predictions.

Context Window: AI’s Memory Limit

LLMs don’t have long-term memory. They rely on a context window, which defines how much text the model can process at once.

• GPT-4 has a 32K token limit
• Claude 3.5 supports 128K tokens

If a conversation exceeds this limit, the model forgets earlier parts.

How AI “Remembers” and Responds

Since AI doesn’t retain past conversations, it generates responses based on:

• Pattern Recognition – Predicting the next word based on probability.
• Attention Mechanisms – Focusing on key parts of the input to maintain coherence.
• Recency Bias – Prioritizing recent tokens over earlier ones.

LLMs don’t “think” like humans, but through these structured steps, they create remarkably human-like responses.