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LLMs and AI Aren't the Same. Everything You Should Know About What's Behind Chatbots 19%
By Lisa Lacy24% Katelyn Chedraoui47%
5/31/2025, 5:00:19 AM
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LLMs and AI Aren't the Same.
Everything You Should Know About What's Behind Chatbots
Chances are, you've heard of the term "large language models," or LLMs, when people are talking about generative AI.
But they aren't quite synonymous with the brand-name chatbots like ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and Anthropic's Claude.
These AI chatbots can produce impressive results, but they don't actually understand the meaning of words the way we do.
Instead, they're the interface we use to interact with large language models.
These underlying technologies are trained to recognize how words are used and which words frequently appear together, so they can predict future words, sentences or paragraphs.
Understanding how LLMs work is key to understanding how AI works.
And as AI becomes increasingly common in our daily online experiences, that's something you ought to know.
This is everything you need to know about LLMs and what they have to do with AI.
What is a language model?
You can think of a language model as a soothsayer for words.
"A language model is something that tries to predict what language looks like that humans produce," said Mark Riedl, professor in the Georgia Tech School of Interactive Computing and associate director of the Georgia Tech Machine Learning Center.
"What makes something a language model is whether it can predict future words given previous words."
This is the basis of autocomplete functionality when you're texting, as well as of AI chatbots.
What is a large language model?
A large language model contains vast amounts of words from a wide array of sources.
These models are measured in what is known as "parameters."
So, what's a parameter?
Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output.
The number of variables in these computations are parameters.
A large language model can have 1 billion parameters or more.
"We know that they're large when they produce a full paragraph of coherent fluid text," Riedl said.
How do large language models learn?
LLMs learn via a core AI process called deep learning.
"It's a lot like when you teach a child -- you show a lot of examples," said Jason Alan Snyder, global CTO of ad agency Momentum Worldwide.
In other words, you feed the LLM a library of content (what's known as training data) such as books, articles, code and social media posts to help it understand how words are used in different contexts, and even the more subtle nuances of language.
The data collection and training practices of AI companies are the subject of some controversy and some lawsuits.
Publishers like The New York Times, artists and other content catalog owners are alleging tech companies have used their copyrighted material without the necessary permissions.
(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed on Ziff Davis copyrights in training and operating its AI systems.)
AI models digest far more than a person could ever read in their lifetime -- something on the order of trillions of tokens.
Tokens help AI models break down and process text.
You can think of an AI model as a reader who needs help.
The model breaks down a sentence into smaller pieces, or tokens -- which are equivalent to four characters in English, or about three-quarters of a word -- so it can understand each piece and then the overall meaning.
From there, the LLM can analyze how words connect and determine which words often appear together.
"It's like building this giant map of word relationships," Snyder said.
"And then it starts to be able to do this really fun, cool thing, and it predicts what the next word is … and it compares the prediction to the actual word in the data and adjusts the internal map based on its accuracy."
This prediction and adjustment happens billions of times, so the LLM is constantly refining its understanding of language and getting better at identifying patterns and predicting future words.
It can even learn concepts and facts from the data to answer questions, generate creative text formats and translate languages.
But they don't understand the meaning of words like we do -- all they know are the statistical relationships.
LLMs also learn to improve their responses through reinforcement learning from human feedback.
"You get a judgment or a preference from humans on which response was better given the input that it was given," said Maarten Sap, assistant professor at the Language Technologies Institute at Carnegie Mellon University.
"And then you can teach the model to improve its responses."
What do large language models do really well?
LLMs are very good at figuring out the connection between words and producing text that sounds natural.
"They take an input, which can often be a set of instructions, like 'Do this for me,' or 'Tell me about this,' or 'Summarize this,' and are able to extract those patterns out of the input and produce a long string of fluid response," Riedl said.
But they have several weaknesses.
Where do large language models struggle?
First, they're not good at telling the truth.
In fact, they sometimes just make stuff up that sounds true, like when ChatGPT cited six fake court cases in a legal brief or when Google's Bard (the predecessor to Gemini) mistakenly credited the James Webb Space Telescope with taking the first pictures of a planet outside of our solar system.
Those are known as hallucinations.
"They are extremely unreliable in the sense that they confabulate and make up things a lot," Sap said.
"They're not trained or designed by any means to spit out anything truthful."
They also struggle with queries that are fundamentally different from anything they've encountered before.
That's because they're focused on finding and responding to patterns.
A good example is a math problem with a unique set of numbers.
"It may not be able to do that calculation correctly because it's not really solving math," Riedl said.
"It is trying to relate your math question to previous examples of math questions that it has seen before."
While they excel at predicting words, they're not good at predicting the future, which includes planning and decision-making.
"The idea of doing planning in the way that humans do it with … thinking about the different contingencies and alternatives and making choices, this seems to be a really hard roadblock for our current large language models right now," Riedl said.
Finally, they struggle with current events because their training data typically only goes up to a certain point in time and anything that happens after that isn't part of their knowledge base.
Because they don't have the capacity to distinguish between what is factually true and what is likely, they can confidently provide incorrect information about current events.
They also don't interact with the world the way we do.
"This makes it difficult for them to grasp the nuances and complexities of current events that often require an understanding of context, social dynamics and real-world consequences," Snyder said.
How are LLMs integrated with search engines?
We're seeing retrieval capabilities evolve beyond what the models have been trained on, including connecting with search engines like Google so the models can conduct web searches and then feed those results into the LLM.
This means they could better understand queries and provide responses that are more timely.
"This helps our linkage models stay current and up-to-date because they can actually look at new information on the internet and bring that in," Riedl said.
That was the goal, for instance, a while back with AI-powered Bing.
Instead of tapping into search engines to enhance its responses, Microsoft looked to AI to improve its own search engine, in part by better understanding the true meaning behind consumer queries and better ranking the results for said queries.
Last November, OpenAI introduced ChatGPT Search, with access to information from some news publishers.
But there are catches.
Web search could make hallucinations worse without adequate fact-checking mechanisms in place.
And LLMs would need to learn how to assess the reliability of web sources before citing them.
Google learned that the hard way with the error-prone debut of its AI Overviews search results.
The search company subsequently refined its AI Overviews results to reduce misleading or potentially dangerous summaries.
But even recent reports have found that AI Overviews can't consistently tell you what year it is.
For more, check out our experts' list of AI essentials and the best chatbots for 2025.
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