Learning about large language models (LLMs) has become more than just a curiosity. For many people, understanding artificial intelligence is necessary to advance their careers. LLMs and generative AI are at the forefront of technology, transforming the way we work.

According to a 2024 PwC study, the number of roles requiring AI skills is increasing rapidly. In the financial services industry, for example, jobs are 2.8x more likely to require AI skills than jobs in other sectors. For jobs in the information and communication fields, jobs are 5x more likely to require AI skills. With these skills in high demand, understanding generative AI and LLMs is the first step in understanding how they can help you advance your career.

Ahead, we break down what LLMs are, what generative AI is and the differences between generative AI and LLMs. We also share some of the applications of these technologies that show how AI language models power various forms of technology.

What Are Large Language Models?

Large language models are AI systems that process large amounts of data and create output in natural language. While they represent a giant leap in terms of technological capabilities, LLMs are rather straightforward in terms of how they work. Breaking down each word in the name can help you understand how they bridge the gap between business and data science:

• Large: The word “large” refers to the large amount of data necessary to train LLMs. The more data provided to an LLM, the more accurate the output.
• Language: One of the reasons LLMs have become so popular over the past few years is the fact that their output uses natural language. Output presented in natural language is easy to understand, making these AI models more accessible.
• Models: This part of the definition refers to the ability of these systems to model human decision-making. In addition to processing large amounts of data, we rely on LLMs to make sense of the data in a logical way.

Although many AI systems we are familiar with today are LLMs, not all generative AI applications rely on LLMs. In the next sections, we break down the meaning of generative AI and what differentiates LLMs from generative AI.

What Is Generative AI?

Generative AI is a type of AI system primarily used to generate content. The content output from generative AI systems can come in the form of images, text, code, video and audio. Generative AI has become increasingly popular because of its applications in business and social media marketing. These applications help businesses and creators increase their content output. But it is important to label that content as AI generated. This way, users can distinguish between images depicting real life and images altered by AI.

What Is the Difference Between Large Language Models and Generative AI?

The difference between large language models and generative AI can best be described with a simple analogy. If LLMs were peach trees, then calling them generative AI would be like calling them trees. Peach trees are a type of tree, so you could use the word “tree” to describe them. But you could not refer to all trees as peach trees. Similarly, generative AI is a type of AI, as trees are a type of plant. The content output generated by LLMs could be considered peach.

Applications of Large Language Models

One of the reasons large language models have become so popular is that they can help users access information via chatbots, for example, rather than waiting to speak to a company representative. This can reduce the need for call centers and reduce wait time for customer service inquiries.

Before large language models, receiving information in natural language usually required you to interact with another person at the moment. Today, these applications of LLMs help us access information anytime, anywhere in a way that is easy to understand.

1. Natural Language Processing and Chatbots

The most common way users interact with LLMs today is through chatbots that use natural language processing (NLP). This includes Siri, Alexa and ChatGPT, all of which rely on NLP to create output using language that is easily understood. Many companies rely on LLMs to answer questions, share updates and troubleshoot problems. This can reduce the need to call centers and other forms of customer service, which cuts costs and reduces human error.

2. Search Engines

Users rely on search engine results to quickly find accurate information that answers their queries. Using LLMs, companies like Google can enhance search results. They can also use this technology to develop insights that can help them update their search engine. Recently, AI results have been incorporated into search results pages, offering users another way to find what they are looking for.

3. Research

Another way professionals have been using LLMs is by conducting research. Since ChatGPT and similar platforms deliver results in natural language, it can help make preliminary research easier than traditional forms of research. Output from LLMs is also making its way into academic research, with an increasing number of peer-reviewed articles using content generated by LLMs. As a result, many academic institutions require attribution to LLMs when using content generated by AI.

Applications of Generative AI

On a larger scale, generative AI has applications that extend beyond the workplace. As the world becomes more connected, applications like translation become more useful for people communicating across cultures. It also assists with scientific research, where categorization and insights help us increase our knowledge of subjects. Applications of generative AI are increasing as the technology evolves, but here are a few of the most common uses today. 

1. Content Creation

The most common use of generative AI is to create content in various forms. Many content professionals use generative AI to create drafts, summarize content and determine tone. Generative AI is also used in medical imaging, helping medical professionals diagnose diseases early. It can also be used to create synthetic data in areas with smaller datasets to pull information from. 

2. Translation

Another common application of generative AI is translation. While Machine Translation (MT) can translate content faster, generative AI can be more efficient in translating a larger volume of data. However, using generative AI for translation can be more cost-effective.

3. Software Coding

Similar to how a writer can use generative AI to assist with writing copy, a programmer can also use generative AI to help them write code. Programming includes a lot of testing and debugging, which is something generative AI can assist with. It can also be used to figure out the right scripts for a specific function or desired output.

4. Categorization and Insights

One of the benefits of generative AI is its ability to categorize data. This can help users generate insights about their performance on social media platforms, in search engine results and with particular customer segments.

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