Large Language Models Explained
How AI Turns Language Into Conversation
Version 2.1
Joe Bjorklund & Jobe Rose
Baird and Warner
Technical Accuracy Statement
This paper explains the fundamental concepts behind large language models (LLMs), the technology that powers conversational AI systems such as ChatGPT, Gemini, Claude, and others. The discussion focuses on principles that are broadly accepted within the fields of machine learning and natural language processing rather than the proprietary implementation details of any individual company.
Although different organizations train and deploy their models in different ways, the concepts presented here describe the common foundations of modern large language models.
Abstract
The public often describes artificial intelligence as if it “knows” answers in the same way a person remembers facts. It is easy to imagine a giant digital encyclopedia hidden inside every AI system, waiting to retrieve the correct paragraph whenever someone asks a question.
That mental picture is understandable, but it is also misleading.
Modern large language models do not retrieve complete answers from an internal library. Instead, they generate language by predicting one small piece of text at a time, using statistical relationships learned during training. This process happens so quickly that the final response appears effortless, even though it is created word by word—or more accurately, token by token.
Understanding this distinction is essential to understanding both the remarkable capabilities and the limitations of today’s AI systems.
1. The Misconception
Ask someone how ChatGPT answered a question, and you will often hear a description that sounds something like this:
“It searched everything it knows and found the answer.”
That explanation feels reasonable because it resembles how traditional software works. Search engines retrieve documents. Databases return stored records. Encyclopedias present information that has already been written.
Large language models work differently.
When you ask an AI system to explain the history of Lincoln Park or compare Chicago neighborhoods, it is not searching through a hidden collection of completed essays waiting to be copied. Instead, it is generating a brand-new response in real time.
Every sentence you read is being created as the model predicts what should come next based on the conversation, its training, and the patterns it has learned from enormous collections of text.
The answer is being constructed while you wait. In short, the AI isn’t reading a finished page to you—it is composing the page as you read
2. What Is a Large Language Model?
A large language model is a machine learning system designed to understand and generate human language.
Its primary objective is surprisingly simple: given everything that has already been written, predict what should come next.
At first glance, that objective appears almost too simple to explain the sophisticated conversations people have with modern AI. Yet this simple prediction task, repeated millions of times during training and thousands of times during a single conversation, produces remarkably capable systems.
The model does not “think” in the same way people think. It does not pause to retrieve paragraphs from memory. Instead, it continually evaluates probabilities and generates language that best fits the context it has been given.
That distinction explains both its strengths and its limitations.
3. Prediction, Not Retrieval
Imagine someone begins a familiar sentence:
“The address of the White House is 1600…”
Most people immediately expect the next word to be Pennsylvania.
Not because they consciously searched a database, but because experience has taught them what usually follows.
Large language models operate on a similar principle, although at a vastly larger scale and with far greater mathematical complexity.
At every step, the model evaluates many possible continuations and assigns probabilities to each of them. It then selects the next token based on those probabilities and repeats the process again. This cycle continues until the response is complete. It does not plan out entire paragraphs in advance; rather, each chosen piece guides what the model considers next.
Although each prediction is relatively small, hundreds of accurate predictions made in sequence can produce explanations, summaries, stories, computer programs, and conversations that appear remarkably natural.
4. Why This Feels Like Intelligence
One of the reasons modern AI surprises people is that prediction does not seem powerful enough to produce thoughtful responses.
Yet language itself contains enormous amounts of structure.
Words relate to one another. Sentences follow patterns. Ideas develop logically. Context influences meaning. By learning these relationships from vast amounts of text, large language models become increasingly effective at generating coherent responses.
The result often resembles reasoning, even though the underlying process is fundamentally different from human thought.
Researchers continue to study exactly how higher-level capabilities emerge from large language models, making this one of the most active areas of artificial intelligence research.
5. Training and Conversation Are Different
One of the easiest ways to misunderstand AI is to confuse training with conversation.
Training happens long before you ask a question. During that process, the model analyzes enormous amounts of text and gradually adjusts billions of internal parameters as it learns statistical relationships between concepts.
Conversation happens afterward.
When you ask a question, the model is no longer learning from that interaction in the same way it learned during training. Instead, it applies what it has already learned to generate a response based on the context currently available.
Understanding this distinction helps explain why language models sometimes answer brilliantly and sometimes confidently produce mistakes. The model isn’t cross-checking its facts in real time; it is relying entirely on patterns internalized during its initial training.
6. Why This Matters for Real Estate
A large language model does not search your website the way a person reads individual pages.
Instead, it analyzes whatever information is available to it and generates responses based on the relationships it identifies within that information.
For real estate professionals, this has an important implication.
Clear, descriptive, well-structured content provides richer context than generic marketing language filled with vague claims. Pages that explain neighborhoods, architecture, market trends, and local knowledge give AI systems substantially more meaningful information than pages that simply repeat promotional phrases.
The goal is not to write for algorithms.
The goal is to communicate clearly enough that both people and AI systems can understand the ideas being presented.
Key Takeaways
Large language models generate language by predicting one token at a time rather than retrieving complete answers from an internal database.
Although the prediction process is conceptually simple, the enormous scale of modern training allows these systems to produce coherent, context-aware responses across an extraordinary range of topics.
Understanding that distinction provides a more accurate mental model of how conversational AI works and helps explain both its impressive capabilities and its limitations.
Questions for the Curious
- If language models predict one token at a time, why do they sometimes appear to reason through complex problems?
- What exactly is a token, and why isn’t it always a word?
- How does an AI system remember earlier parts of a conversation?
- If a model cannot access new information on its own, how do retrieval systems keep its answers current?
These questions form the basis of the next papers in the AI Reference Library.
See these concepts applied to real questions in our monthly AI Benchmark Run series, comparing ChatGPT, Claude, and Gemini on the same Chicago real estate questions.