What Are Large Language Models (LLMs) & How Do They Work?

By Kelsey Miller on July 14, 2026

Hands typing on a laptop with a digital illustration representing a large language model (LLM) and connected AI functions.

If you've ever used a chatbot like ChatGPT, Claude, or Gemini, you've already interacted with a large language model (LLM).

These tools are transforming how companies research, write, code, analyze information, and make decisions. Yet, the technology behind them can still feel like a black box.

This guide explains what LLMs are, how they work, and what business leaders must understand to use them effectively.

What Are LLMs?

Large language models (LLMs) are AI models trained on enormous collections of text—including books, articles, websites, and research papers—to process, predict, and generate human language. By learning statistical patterns across this data, LLMs produce responses that closely resemble human communication.

At its core, an LLM performs a surprisingly simple task: predict the next token—a word or part of a word—in a sequence based on everything that came before it. When done well and at a large enough scale, the result is a model capable of holding conversations, summarizing reports, drafting emails, and explaining complex topics in simple terms.

“These models don't just analyze existing data—they create new data,” says Harvard Business School Professor Karim Lakhani in the online course Data Science and AI for Decision Making, which he co-teaches alongside HBS Professor Iavor Bojinov. “By repeatedly predicting the next word, sound, or pixel, they can write, summarize, translate, code, and even simulate reasoning.”

It's important to distinguish an LLM from the applications built on top of it. For example, ChatGPT is a product built on top of an LLM—in this case, one of OpenAI's GPT models. Think of an LLM as the engine and the chat application as the vehicle that makes it useful.

How Do LLMs Work?

You don’t need a technical background to understand the basics of how LLMs work, but there are several key concepts you should familiarize yourself with.

Tokenization and Embedding

Before a model can process text, it converts words and word fragments into units called tokens. For example, a common word like "marketing" may be represented by a single token, while a less common word or technical phrase may be split into multiple tokens. Rather than processing language the way humans do, an LLM works with sequences of tokens and learns the relationships between them.

“After tokens are created, each token is transformed into a numerical representation known as a word embedding,” Lakhani explains in Data Science and AI for Decision Making. “Think of it as placing each token into a multi-dimensional space. Words with similar meanings end up close together in this space—so ‘take’ might be near ‘grab,’ but far from ‘we.’ These embeddings help the model understand context and relationships among words.”

Once language has been converted into tokens and embeddings, the model can begin identifying patterns and relationships across a sentence, paragraph, or entire conversation.

The Transformer Architecture

Most modern LLMs are built on an architecture called the transformer, first introduced by Google researchers in 2017.

Earlier language models processed text more sequentially, making it difficult to capture relationships across long passages. Transformers made it possible for AI systems to consider relationships among words throughout an entire sentence, paragraph, or document.

“What makes [transformers] so powerful is that they don’t just process words one-by-one—they use a mechanism called self-attention to look at all the words in a sentence at once and decide which ones are important for predicting the next word,” Bojinov explains in Data Science and AI for Decision Making.

For example, this is what allows an LLM to understand that, in the statement "the bank raised its rates," the word “bank” refers to a financial institution rather than a riverbank. By considering the surrounding words, the model can determine which meaning best fits the context.

“In short, transformers don’t just look at words—they learn how words relate to one another in context, across entire sentences or documents," Bojinov says in Data Science and AI for Decision Making. "And that’s what enables generative models to write, summarize, translate, and reason the way they do."

Predicting, One Token at a Time

When you ask an LLM a question, it generates a response by predicting the most likely next token, adding it to the sequence, then predicting the next one, and so on until it produces a complete answer.

Although the response feels instantaneous, the model is performing this prediction process many times per second based on patterns learned during training.

Fine-Tuning and Alignment

A pretrained model is powerful, but it doesn’t come without limitations. Even though an LLM can generate well-structured outputs, it isn’t automatically aligned with human preferences, organizational goals, or ethical standards.

AI developers—and in some cases, organizations deploying LLMs—refine models through a process called fine-tuning. This typically involves providing the model with examples and feedback, often from human reviewers, about the quality of its responses so it can generate more accurate, useful, and safer outputs.

What Can LLMs Actually Do?

In practice, these capabilities enable LLMs to support a wide range of business tasks. They can:

Draft, edit, and summarize written content
Answer questions and explain concepts across a wide range of topics
Generate, explain, and debug computer code
Analyze, synthesize, and organize information from large volumes of text
Translate text between languages or adapt it to different formats
Extract, classify, and structure information from documents, spreadsheets, and other data sources
Assist with brainstorming, planning, researching, and decision-making

Despite these capabilities, LLMs don't understand language the way people do. They don't have beliefs, lived experience, or a built-in ability to distinguish truth from falsehood. Instead, they generate responses based on statistical patterns learned during training.

As a result, LLMs can sometimes produce responses that sound confident but are inaccurate or entirely fabricated—a phenomenon known as AI hallucinations. For that reason, business leaders should treat LLM-generated content as a starting point, verifying important facts, calculations, and sources before relying on them for high-stakes decisions.

LLM Limitations and Risks

Understanding what LLMs are, how they work, and what they can be used for is only part of the puzzle. Leveraging them effectively also means knowing where they fall short—and why.

“AI is not magic,” says Marily Nika, an AI Product Lead at Google, in an interview for Data Science and AI for Decision Making. "It's very powerful, but it's really built on data, on assumptions, on hypotheses, and trade-offs. So understanding the limitations alongside the potential really helps people and leaders engage with it more critically and responsibly.”

Hallucinations, for example, stem from how LLMs generate responses. “The model isn’t trying to be correct; it’s trying to be probable,” Lakhani says in Data Science and AI for Decision Making. “It’s not evaluating whether a fact is true; it’s choosing what sounds right based on patterns it's seen.”

LLMs can also make assumptions and reflect biases present in training data, which are reinforced in their outputs. In a business setting, this can have serious consequences, particularly in high-stakes applications, such as financial analysis, legal research, hiring decisions, or tasks involving sensitive company data.

Fine-tuning and other alignment techniques help mitigate these issues, but they don't eliminate them. Alignment is an ongoing effort, not a one-time fix, and even well-aligned models can behave unpredictably in certain circumstances.

That's why business leaders need a foundational understanding of how LLMs work. Even if they never build or fine-tune a model themselves, they're increasingly responsible for selecting AI tools, setting policies for their use, and ensuring they're deployed responsibly across their organizations.

Mastering LLMs as a Business Skill

Mastering LLMs in a business context requires sound judgment. That means knowing where they add the most value—drafting content, synthesizing information, and conducting first-pass analysis—and where they still require close human oversight, such as high-stakes decisions, fact-intensive work, or nuanced judgment.

This kind of AI fluency is quickly becoming an expectation rather than a specialized technical skill. Leaders who understand how these models work and where their limits lie are better positioned to integrate these tools responsibly into their teams' workflows.

Building a strong foundation in data literacy, analytics, and AI strategy can help professionals move beyond using AI tools to applying them with confidence and purpose. As LLMs continue to transform how work gets done, those skills will become increasingly valuable across every industry.

Are you ready to improve your ability to leverage data and artificial intelligence in your business? Explore Data Science and AI for Decision Making—one of our online digital transformation and AI courses—or download our free flowchart to choose the right course for your goals and needs.

Author:

Kelsey Miller

Topics:

Digital Transformation & AI