Inside Large Language Models: Revealing How LLM Technology Actually Works

Overview

A deep dive into the inner workings of Large Language Models—how they understand, generate, and interact with human language using cutting-edge AI technology.

What is a Large Language Model (LLM)?

Large Language Models (LLMs) are advanced artificial intelligence systems trained on massive text datasets from books, websites, and other sources to understand and generate human language. LLMs learn patterns and relationships between words by analyzing billions of examples, allowing them to predict what text should come next in any given context. 

Working through neural networks called transformers, these models process information in parallel rather than sequentially, enabling them to maintain context across longer passages. 

Many popular AI tools like ChatGPT, Claude, Gemini, Bard, and Llama fall into this category, demonstrating remarkable abilities to answer questions, write essays, translate languages, and even assist with coding tasks—all by predicting the most statistically likely words to follow your prompt.

What Makes an LLM Different from Traditional Programming

Unlike conventional computer programs that follow explicit instructions, Large Language Models learn patterns from massive text datasets spanning books, websites, code repositories, and more. Traditional software operates through predetermined rules—if X happens, do Y. LLMs instead build statistical associations between words and concepts across billions of examples.

A helpful analogy -  imagine traditional programming as following a detailed recipe with exact measurements and steps. LLMs function more like a chef who's eaten thousands of dishes and can create new recipes based on patterns they've absorbed. The model doesn't truly "understand" language any more than the chef "understands" molecular gastronomy—both work through pattern recognition refined through extensive experience.

The difference becomes apparent in flexibility. Rule-based programs break when encountering situations not explicitly coded for, while LLMs often handle novel inputs reasonably well by drawing on statistical similarities to their training data.

Tokenization - The Foundation of LLM Technology

Tokenization transforms human language into machine-processable numbers, forming the first crucial step in how LLM technology works. Since computers process numbers, not words, LLMs convert text into numerical tokens that might represent entire words, parts of words, or even punctuation marks.

For example, "college student" might become [4872] [2391] internally, with each token assigned a specific numerical ID. Commonly used words often get their own tokens, while rare words get split into smaller pieces. The word "entrepreneurship" might become multiple tokens like [enter, pre, neur, ship].

After tokenization, LLMs transform these numerical IDs into multi-dimensional vectors called embeddings, creating a semantic map where similar concepts cluster together. Words like "university" and "college" position closer to each other than "university" and "banana" in this mathematical space.

Entrepreneurs using AI tools should understand this tokenization process affects how models interpret specialized business terminology. Industry jargon or technical terms might get fragmented into less meaningful pieces unless the model was specifically trained on relevant domain knowledge.

The Transformer Architecture - The Engine Behind Modern LLMs

Self-attention mechanisms revolutionized language processing by allowing models to weigh connections between words based on context. The breakthrough 2017 Transformer architecture powers virtually all modern Large Language Models through several key components:

• Encoder layers process input text into rich contextual representations

• Decoder layers generate output based on encoded information

• Multi-head attention allows simultaneous focus on different aspects of text

• Feed-forward neural networks transform information between layers

The distinctive capability of Transformers lies in parallel processing—unlike earlier models that processed text sequentially (word by word), Transformers analyze entire sequences simultaneously, considering relationships between all words at once.

For business applications, this architecture enables LLMs to maintain context across long documents, connect concepts across paragraphs, and generate coherent long-form content that maintains thematic consistency—capabilities essential for tasks like contract analysis, report generation, or marketing copy creation.

How LLMs Generate Text - Predicting One Token at a Time

Probability distributions determine what comes next when LLM works with text generation. Given an input prompt, the model:

1. Tokenizes the prompt into numerical representations

2. Processes tokens through neural network layers

3. Calculates probabilities for possible next tokens

4. Selects the next token based on these probabilities

5. Adds the selected token to the sequence

6. Repeats until reaching a stopping condition

Temperature settings control randomness—higher values produce more creative but potentially less accurate outputs, while lower values create more predictable, conservative responses.

Business leaders should recognize how these generation mechanics affect practical applications. Customer service chatbots might use lower temperatures for factual consistency, while marketing content generation might benefit from higher temperatures for creativity.

Building Future-Ready Skills at Tetr

For students fascinated by how LLM technology works, developing relevant skills represents a crucial advantage in tomorrow's economy. Understanding AI fundamentals requires strong foundations in mathematics, computer science, and even linguistics.

Tetr’s Bachelor of Science in Artificial Intelligence is one of the most innovative undergraduate machine learning programs, blending academic rigor with global exposure across seven countries. This four-year program emphasizes hands-on learning through projects like:

• Building AI-powered e-commerce platforms in Dubai (Term 1).

• Developing NGO-donor matching apps using natural language processing in Ghana (Term 4).

Graduates receive both a Bachelor’s degree from Illinois Tech and an undergraduate certificate from Tetr, preparing them for leadership roles in the global tech ecosystem.

FAQ

What makes LLMs different from previous AI systems?

Scale and architecture distinguish modern Large Language Models. Earlier systems used smaller neural networks with different architectures that processed text sequentially. Modern LLMs utilize the parallel processing power of Transformers and contain hundreds of billions of parameters trained on vastly larger datasets, enabling much more sophisticated pattern recognition.

Can LLMs truly understand text?

Large Language Models lack genuine understanding despite impressive outputs. Models predict statistical patterns rather than comprehend meaning—more like sophisticated autocomplete systems than conscious entities. LLMs have no awareness, intentions, or beliefs; their seemingly intelligent responses emerge purely from statistical correlations observed across training data.

What are the environmental impacts of training LLMs?

Training cutting-edge Large Language Models requires enormous computing resources with significant environmental footprints. Leading models use thousands of specialized GPUs running continuously for months, consuming electricity equivalent to hundreds of average households annually. Running a trained model requires substantially less but still significant computing power, especially for larger models with billions of parameters.

How are LLMs evolving?

LLM technology advances continue through several approaches: increased model size (more parameters), multimodal capabilities (processing images/audio alongside text), retrieval-augmented generation (connecting to external knowledge sources), and alignment techniques (making models more helpful and less harmful). Research focuses on reducing hallucinations, improving reasoning, enabling continuous learning, and developing more efficient architectures requiring less computing power.

What ethical concerns surround LLM development?

Ethical considerations include potential job displacement, privacy concerns from models trained on personal data, misinformation risks from convincing but potentially false content generation, bias amplification from training data, and dual-use capabilities that could enable harmful applications. Addressing these challenges requires collaboration between technologists, ethicists, policymakers, and affected communities.