LLM is different from AI. Everything you should know about everything behind a chatbot

When people talk about generating AI, you may have heard of the term “big language model” or LLM. But they are not synonymous with branded 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 in the way we do it. Instead, they are the interfaces we use to interact with large language models. These basic techniques are trained to recognize how words are used and which words often appear so they can predict future words, sentences, or paragraphs. Understanding how LLM works is key to understanding how AI works. As AI becomes more common in our everyday online experience, you should know this.

Here’s everything you need to know about LLMS and how it relates to AI.

What is a language model?

You can think of language models as a diviner of words.

“Language models try to predict languages ​​produced by humans,” said Mark Riedl, professor at the Georgia Institute of Technology’s School of Interactive Computing and associate director of the Georgia Center for Technology Machine Learning. “What makes a certain language model is whether it can predict future words of previous words.”

This is the basis for automatically completing functions when sending text messages and AI chatbots.

What is a big language model?

Large language models contain a large number of words from various sources. These models are based on what is called “parameters”.

So, what are parameters?

Well, LLM uses neural networks, which are machine learning models that perform inputs and perform mathematical calculations to produce outputs. The number of variables in these calculations is parameters. Large language models can have 1 billion or more parameters.

“We know that when they make full and coherent passages of fluid text, they’re big,” Riddle said.

How to learn large language models?

LLM learns through a core AI process called deep learning.

“It’s a lot like when you teach your kids – you show a lot of examples,” said Jason Alan Snyder, global CTO of global advertising agencies.

In other words, you can provide an LLM with a library of content (so-called training data), such as books, articles, codes, and social media posts to help it understand how to use words in different contexts, even more subtle linguistic nuances. Data collection and training practices of AI companies are the subject of some controversy and some litigation. Distributors like The New York Times, artists and other content catalog owners have accused the tech company of using its copyrighted material without the necessary licensing.

(Disclosure: CNET’s parent company Ziff Davis filed a lawsuit against OpenAI in April, accusing it of infringing on Ziff Davis’ copyright in training and operating its AI systems.)

The digestion of AI models is far more than a person can read in his lifetime – in the order of trillions of tokens. Tokens help AI models decompose and process text. You can think of AI models as readers who need help. The model breaks down a sentence into smaller parts or tokens (equal to four characters in English, or three-quarters of a word), so it can understand each part and then understand the overall meaning.

From there, LLM can analyze how words are connected and determine which words often appear together.

“It’s like building this huge map of word relationships,” Snyder said. “Then it starts to be able to do this really fun, cool things and predict what the next word is…it compares the predictions to the actual words in the data and adjusts the internal map based on its accuracy.”

This prediction and adjustment took place billions of times, so LLM continually refines its understanding of language and becomes better at recognizing 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 is statistical relationships.

LLM also learns to improve their response by learning from human feedback.

“You get human judgment or preference,” said Maarten SAP, assistant professor at the Carnegie Mellon University’s School of Language Technology, said. “You can then teach the model to improve its response.”

What does a large language model do?

Given a series of input words, LLM will predict the next word in sequence.

For example, consider “I went to the dark blue…”

Most people may guess “sea” because sailing, dark blue is our words related to the sea. In other words, each word sets the context for what should happen next.

“These large language models, because they have a lot of parameters, can store a lot of patterns,” Riedl said. “They are very good at being able to pick these clues and make very, very good guesses on what will happen next.”

Which language model is?

You may have heard of several subcategories such as small reasoning and open source/open weight. Some of these models are multimodal, meaning they are trained not only on text but also on images, videos, and audio. They are both language models and perform the same functions, but you should be aware of some key differences.

Is there a small language model?

Yes. Tech companies like Microsoft have introduced smaller models that are designed to operate “on the device” and do not require the same computing resources that LLM does, but can still help users leverage the power of generating AI.

What is an AI inference model?

The inference model is an LLM. These models allow you to peek into the curtains in the chatbot’s thought train as you answer your questions. If you use the Chinese AI chatbot DeepSeek, you may have seen this process already.

But what about open source and open models?

Nevertheless, LLM! These models are designed to be more transparent about how they work. Open source models allow anyone to see how the model is built and are often available for anyone to customize and build a model. An open model gives us an understanding of how the model weighs specific characteristics when making decisions.

Are large language models really doing well?

LLM is very good at figuring out the connections between words and the connections between texts that sound natural.

“They take inputs, usually a set of instructions, like doing for me or telling me this or summarizing it, and being able to extract those patterns from the inputs and produce a series of fluid responses.”

But they have several weaknesses.

Where do large language models struggle?

First of all, they are not good at telling the truth. In fact, they sometimes just make up something that sounds real, for example, when chatgpt quotes six fake court cases in the legal summary, or when Google’s Bard (the predecessor of Gemini) (the predecessor of Gemini) mistakenly attributes James Webb Space TeleScope to take the first photo of the planet outside our solar system. These are called hallucinations.

“In a sense, they are very unreliable. “They are not trained or designed in any way to spit out anything real.” ”

They are also struggling with queries that are fundamentally different from anything they have encountered before. That’s because they focus on finding and responding to patterns.

A good example is a mathematical problem with unique numbers.

“Because it doesn’t really solve math, it may not do the calculation correctly,” Riedl said. “It tries to connect your math problem with examples of math problems you’ve seen before.”

Although they are good at predicting words, they are not good at predicting the future, which includes planning and decision making.

“The idea of ​​planning in a human way … thinking about different contingencies and alternatives and making choices seems like a difficult obstacle to our current large language model at the moment,” Riddle said.

Finally, they struggle with the current event because their training data can usually only rise to a certain point in time, and nothing that happens after that is not part of their knowledge base. Because they do not have the ability to distinguish between the facts and the possibility of possible, they can confidently provide misinformation about current events.

Nor do they interact with the world the way we do this.

“This makes it difficult for them to grasp the nuances and complexities of current events, which often require understanding of context, social dynamics and the consequences of the real world,” Snyder said.

How does LLM integrate with search engines?

We have seen the development of search capabilities beyond the training of models, including connecting with search engines like Google so that the models can do web searches and then feeding these results into the LLM. This means they can better understand queries and provide more timely responses.

“This helps our link models stay up to date and up to date, because they can actually view new information on the internet and bring it in,” Riedl said.

For example, this is a goal, AI-powered bing. Instead of leveraging search engines to enhance its response, Microsoft sought to AI to improve its search engine, partly because it better understands the true meaning behind consumer queries and better ranks the results of the above queries. Last November, OpenAI launched ChatGpt Search and accessed information from some news publishers.

But there are catches. Web searches may make hallucinations worse without sufficient fact-checking mechanisms. LLM needs to learn how to evaluate the reliability of a network source before citing it. Google learns that it’s a tough way to make a mistake-prone debut with its AI. The search company then refines its AI overview results to reduce misleading or potentially dangerous summary. But even recent reports have found that AI overviews can’t consistently tell you which year.

For more information, check out our expert list of AI Essentials and the best chatbots of 2025.