Introduction

Large Language Models (LLMs) are the core AI architectures behind today’s human–machine language interactions. An overview of what these models are, how they are structured, and how they are positioned in business contexts is available in the first article of this series.

This article focuses on how large language models process language. Behind what appears to be a natural conversation lies a set of technical mechanisms that break text into units, convert them into numerical representations, manage context, and generate output based on probabilities. In this context, we will examine the following core concepts:

• Tokenization
• Embeddings
• Context Window
• Probabilistic Language Modeling
• Sequence Prediction
• Sampling Strategies

Taken together, these mechanisms explain how large language models work with language not as isolated words, but as patterns of meaning shaped by context and probability. Each plays a distinct role in how text is interpreted and generated. The sections below examine these components step by step, while keeping their interdependencies in view.

1. Tokenization

Large language models do not process text as complete words in the way humans perceive them. Instead, text is broken down into tokens—the smallest computational units the model can process.

A token may be:

• a complete word (“automation”),
• part of a word (“auto” + “mation”),
• or a punctuation mark (“.”, “,”).

This process typically relies on subword tokenization methods. Common approaches include:

• Byte Pair Encoding (BPE): Builds tokens by merging frequently occurring character or subword pairs.
• SentencePiece: A tokenization framework that applies algorithms such as BPE or the Unigram Language Model without relying on predefined word boundaries

The Unigram Language Model is a probabilistic approach that selects the tokenization scheme with the highest overall likelihood among multiple possible segmentations. Its language-agnostic nature makes it particularly suitable for multilingual models.

Example:

Sentence: “Data analysis improves business decisions.”

Tokens: ["Data", "analysis", "improves", "business", "decisions", "."]

In agglutinative languages such as Turkish, a single word may be represented as one token or split into multiple sub-tokens. This enables the model to learn both root forms and the semantic contributions of suffixes.

2. Embeddings

Once tokenized, text is transformed into embeddings—numerical vector representations that the model can process mathematically. Each token is mapped to a fixed-size vector in a high-dimensional vector space.

Within this space:

• semantically similar usages are positioned closer together,
• unrelated concepts are located further apart.

In modern large language models, embeddings are context-dependent. The same token can be represented by different vectors depending on how it is used in a sentence.

Example:

The word “premium” may be embedded differently when used in:

• a payroll or compensation context,
• an insurance or risk context.

Why this matters

Contextual embeddings allow models to go beyond exact word matching. Even if a specific term does not appear in the input, the model can recognize related expressions and generate responses based on semantic similarity rather than surface form.

3. Context Window

The context window defines the maximum number of tokens a model can consider at a single time. It effectively sets the limit for how much text the model can “see” when generating or interpreting output.

Modern large language models can operate on contexts ranging from tens of thousands to, in some cases, hundreds of thousands of tokens. However, this window is finite, and models do not possess long-term memory on their own

When working with long documents or extended conversations, context management is typically handled at the application level. Older content may be summarized, and key information reintroduced into the active context. In practice, this is often referred to as summary-based context management.

Why this matters

• Reduces information loss in long interactions
• Helps preserve key definitions and numerical details
• Enables more focused and coherent responses

Probabilistic Language Modeling

Large language models do not learn language by understanding meaning in a human sense. Instead, they learn probabilistic patterns from massive collections of text.

During training, models observe how tokens co-occur across contexts and learn statistical relationships between them. During generation, the model:

• calculates probabilities for possible next tokens,
• evaluates these probabilities in relation to the current context.

This probabilistic approach allows models to generalize to new expressions they have never seen before. In rare cases, however, models may reproduce specific phrases from their training data. For this reason, enterprise use cases typically include additional safeguards and human oversight.

5. Sequence Prediction

Large language models generate text using an autoregressive approach. At each step, the model considers the current context and predicts the most likely continuation. The selected token is added to the text, forming the input for the next prediction step. This process continues until a natural stopping point is reached.

Example:

Prompt: “Artificial intelligence in the business world…”

Based on learned usage patterns, the model recognizes that this phrase is frequently completed with expressions such as “plays an important role.” Rather than selecting words independently, it favors coherent and commonly occurring phrase structures that align with the context.

6. Sampling Strategies

Token selection during text generation is influenced not only by probabilities, but also by sampling strategies, which directly affect output consistency and variability.

Common strategies include:

• Greedy Search: Always selects the highest-probability token.
• Advantage: Consistency
• Limitation: Predictability
• Temperature: Controls how sharply or broadly probabilities are distributed.
• Lower values produce more deterministic output
• Higher values increase variation and creativity
• Top-k Sampling: Limits selection to the k most likely tokens.
• Top-p (Nucleus Sampling): Selects from the smallest group of tokens whose cumulative probability reaches a defined threshold.

These strategies can be applied individually or in combination, depending on the intended use case.

7. Challenges and Nuances in Language Understanding

Natural language contains inherent ambiguities, and large language models must navigate these complexities:

• Polysemy: A single word may have multiple meanings
• Idioms and figurative language: Meaning extends beyond literal interpretation
• Contextual ambiguity: Incomplete or vague input
• Coreference resolution: Determining what pronouns such as “it” or “they” refer to

In such cases, models may generate responses that appear plausible but are incorrect. This makes human review especially important in professional and enterprise settings.

8. Why This Matters for Business

Understanding these mechanisms helps organizations position large language models not as sources of absolute truth, but as probability-based decision support tools.

This perspective supports:

• clearer and more effective prompt design
• better context management
• more informed evaluation of generated output

Typical application areas include:

• HR: Drafting balanced and inclusive job descriptions
• Compliance: Quickly reviewing key themes and concepts in internal policy documents
• Customer support: Producing consistent, multilingual responses

Next in the Series: How Large Language Models Are Trained

The next article in this series will focus on how these mechanisms are learned. We will examine training processes, data types, scaling approaches, and the role of human feedback. The concepts discussed in this article provide the foundation for understanding that process.