How Do Large Language Models Work? How AI Understands and Generates Text

Key takeaways

Large language models can understand and generate humanlike text using a combination of technologies such as deep learning and autoregressive models.

• LLMs use transformer models to understand the context of words and how words relate to one another.

• The autoregressive model, found within transformer architecture, helps LLMs determine the best words to use in responses.

• You can use or implement LLMs in roles such as data scientist, NLP engineer, and machine learning engineer. 

Discover how a large language model works, explore careers working with this advanced technology, and read about the benefits and challenges that LLMs offer. Afterward, if you’re ready to learn how to build and train deep neural networks, enroll in the Deep Learning Specialization. You’ll also have the opportunity to learn how to use neural style transfer to generate art and apply algorithms to image and video data.

How do large language models work fundamentally?

Large language models use several different layers of other technology, including deep learning, transformer models, and, specifically, the autoregressive models within the transformer models. Take a closer look at these topics and how they work together to power large language models. 

LLM and deep learning

Large language models can generate text that looks natural using deep learning. Deep learning is a type of machine learning (ML) that uses neural networks to analyze their own responses against a vast array of training data to learn how to give a better answer. It is different from other kinds of machine learning because it uses neural networks with huge amounts of layers that replicate the complicated process that the human brain undertakes when it thinks. 

Transformer models

The type of neural networks that LLMs use is transformer models, skilled at understanding the context of words and how words relate to one another. Transformer architecture allows LLMs to generate text by understanding what words are most likely to come next, using principles of natural language processing. This makes the LLM better able to understand the nuances of both the prompt you offer and the sentences or paragraphs it generates as a response. 

Autoregressive models

If you zoom in on an even closer level and look inside the transformer model, you can find another technology contributing to LLMs: the autoregressive model. The autoregressive model is found within transformer architecture and helps the LLM determine the best words to use in its response based on predictions it gathers from its training material. For example, imagine you ask an LLM, “What color is grass?” The LLM isn’t looking through its training material the way you might flip through a textbook to find the answer. Instead, it uses autoregressive models to determine that “grass is green” is statistically the answer you’re looking for. 

Is ChatGPT an LLM or generative AI?

OpenAI’s ChatGPT is an LLM that generates natural-language text in response to a user prompt. All LLMs are a specialized subset of generative AI. However, not all generative AI systems are LLMs.

Read more: Large Language Models (LLMs) vs. Generative AI: What’s the Difference?

What is a large language model used for?

Large language models offer many different uses and applications in many industries, which is part of why this new technology has created so much interest. Some of the ways you can use large language models include: 

• Automated content creation: An LLM can generate content you can use in other settings. 

• Chatbots and virtual assistants: You can train a large language model to answer customer service questions and provide general knowledge.

• Sentiment analysis: You can share text with an LLM and ask it to analyze the emotions used before the words. 

• Research: LLMs can help provide data analysis that makes research quicker. 

Who uses large language models?

As you explored above, you can use large language models in various use cases to help you with tasks in multiple industries. If you’re interested in exploring careers directly related to large language models, three potential careers include data scientist, NLP engineer, and machine learning engineer. 

Data scientist

Annual median total US pay (Glassdoor): $156,000 [1]

Job outlook (projected growth from 2024 to 2034): 34 percent [2]

As a data scientist, you use data to solve problems. In this role, you may focus on developing and fine-tuning large language models for various applications, such as natural language understanding, text generation, and more complex AI tasks. You can do this by building algorithms that help guide machine learning. 

NLP engineer

Annual median total US pay (Glassdoor): $164,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [2]

As a natural language processing engineer, you work with a team to create 

NLP systems, defining data sets for training, implementing algorithms, and working on AI speech pattern recognition. Depending on the industry you work in and the goals of the program you’re engineering, your day-to-day responsibilities could look different.

Machine learning engineer

Annual median total US pay (Glassdoor): $162,000 [4]

Job outlook (projected growth from 2024 to 2034): 20 percent [5]

As a machine learning engineer, you work with your team to create machine learning solutions to problems for your company or client. In this role, you will likely research machine learning and use programming languages to write new ML applications. You may also spend time testing or training machine learning algorithms. 

All salary information represents the median total pay from Glassdoor as of June 2026. These figures include base salary and additional pay, which may represent profit sharing, commissions, bonuses, or other compensation.

Benefits and challenges of using large language models

Large language models offer many benefits to us, but they also bring challenges for researchers and AI professionals to overcome. Large language models are good at what they do and are flexible enough that you can adapt them to lots of different use cases. You can fine-tune a model to perform a specific function, which can improve its performance and accuracy. However, one of the more exciting aspects of LLMs is what comes next. This foundational technology can lay the framework for even more advanced technology in the future. 

At the same time, it’s important to remember that the technology faces challenges in its current state. First of all, a model is only as good as the data it trains on, which can mean that AI models regurgitate bias or ethical concerns present in training materials and present those biases as fact. LLMs can also “hallucinate,” or make up answers that aren’t factual. 

The environmental impact of generative AI and large language models can also be a benefit and a challenge. The power consumption required to operate a large language model prompt is substantial. But LLMs could also offer environmental benefits, such as an increased ability to promote environmental education, reducing language barriers worldwide, and increasing human productivity. Researchers and AI professionals should weigh the risks and benefits of these technologies as they develop.

Explore our free career-building resources

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• Structure your learning: Natural Language Processing Learning Roadmap: Beginner to Expert

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