Home > What Is a Large Language Model (LLM) and How Does It Work?

What Is a Large Language Model (LLM) and How Does It Work?

Large language models are becoming a part of everyday business operations, changing the way we work, learn, communicate, and create. As these technologies become more common, understanding what they are, how they work, and how they fit into the broader field of artificial intelligence is increasingly valuable.

If you’re interested in pursuing a career in AI, a Master’s Degree in Artificial Intelligence from MIU can help you build a strong foundation of skills and technical knowledge combined with practical business applications.

What is an LLM and how does it relate to artificial intelligence?

So, you may be asking yourself, what is an LLM? Or what is a large language model? Without knowing it, you’ve already interacted with a large language model if you’ve ever used ChatGPT or another AI assistant. An LLM is an AI model that’s trained to understand and generate human language.

The meaning of LLM is, quite simply, “large language model,” and the definition of a large language model is an artificial intelligence system that’s trained on massive collections of text in order to learn patterns, context, grammar, and the relationships between words.

These systems use machine learning to predict the most likely sequence of words in order to answer questions, summarize information, write code, and perform many other language-based tasks we ask them to.

How does a large language model work?

So, how do LLMs work? The technology is highly sophisticated, but the process itself is pretty straightforward. They’re trained on huge text datasets to learn the relationships between words and phrases, instead of just storing fixed answers. So, when you enter a prompt, the model predicts the most likely next word until it produces a complete response.

Most AI language models today use Transformer-based large language model architecture, meaning they can process long passages and better understand text.

What are the main types of language models?

Open-source vs proprietary models

Each LLM model is designed for a particular need. Open-source large language models help organizations customize and deploy software themselves. Proprietary models, on the other hand, are developed by private companies, and organizations typically access them through cloud services or an API.

Multimodal models

Multimodal large language models can do more than just process text; many of them can also analyze images or audio and generate natural language responses.

Models by specialization

Some LLMs are general-purpose assistants. Others, however, are more specific and optimized for fields like healthcare, finance, software development, or legal services.

Models by size

The size of LLMs varies as well, according to the number of parameters they contain. Larger models usually work better across a wider range of tasks. However, they also need more computing resources.

What are some examples of LLMs?

If you’ve ever wondered what is GPT, it’s one of the best-known examples of a large language model. The following table provides details about GPT and several other common LLMs:

Model	Developer	Typical uses
GPT	OpenAI	Writing, coding, research
Gemini	Google	Search, productivity, multimodal tasks
Llama	Meta	Research, customization
Claude	Anthropic	Writing, reasoning, document analysis
Mistral	Mistral AI	Business applications, custom AI solutions

What are the applications of LLMs in business?

Organizations are constantly finding new LLM applications to improve their operations, including the following:

Automating customer support
Creating marketing and business content
Assisting software developers
Summarizing reports and lengthy documents
Organizing internal knowledge bases
Analyzing data and generating reports

When businesses combine large language models with other artificial intelligence tools, they can automate workflows and improve productivity even more.

What are the limitations and risks of LLMs?

LLMs aren’t perfect; they can provide inaccurate information, misinterpret context, and even reflect biases that are present in training data. There are also privacy, copyright, security, and responsible AI governance issues that organizations need to address when they implement them.

AI-generated content should always be reviewed by a human, especially in fields like healthcare, finance, law, and education.

What is the difference between an LLM and generative AI?

It’s easy to confuse these two terms, especially because they’re closely related. According to UNESCO, “The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content.”

Source: TVETipedia glossary

In other words, Generative AI is a broader category of tech that’s capable of creating text, images, audio, code, and other types of content. LLMs are more specific in that they’re generative AI that understands and produces natural human language.

How to build a career in artificial intelligence?

AI is evolving rapidly, and the demand for professionals who are familiar with machine learning, data science, programming, and AI ethics is increasing.

Graduate programs like MIU’s Master’s Degree in Artificial Intelligence are an excellent way to develop technical skills and prepare for careers in the world of artificial intelligence.

References

National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative AI Profile (AI 600-1). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

Organisation for Economic Co-operation and Development. (2024). AI language models. https://www.oecd.org/en/publications/ai-language-models_13d38f92-en.html

Organisation for Economic Co-operation and Development. (2024). OECD Digital Economy Outlook 2024 (Vol. 1). https://www.oecd.org/en/publications/2024/05/oecd-digital-economy-outlook-2024-volume-1_d30a04c9/full-report/component-5.html

UNESCO-UNEVOC. (n.d.). TVETipedia Glossary: Large language models (LLM). https://unevoc.unesco.org/home/TVETipedia%2BGlossary/lang%3De/show%3Dterm/term%3Dlarge%2Blanguage%2Bmodels