Generative AI vs. Large Language Models: What’s the Difference?

Generative artificial intelligence (GAI) and large language models (LLMs) are both forms of artificial intelligence systems. However, GAI refers to any artificial intelligence system used to generate content, while LLMs can understand and generate language. Although these two systems are different, some technologies encapsulate both, such as Open AI’s GPT-4. Learn more about GAI versus LLMs and what these technologies are capable of.

Generative AI

GAI is a deep learning model capable of generating various forms of content, such as text, video, images, and even music, depending on the training data it receives. Rather than taking training data and using it to make predictions like previous AI models, GAI instead uses the training data to learn to create its own unique, but similar data. For example, you could give a GAI model a set of data consisting of pictures of cats. The model would then learn to recognize patterns and the distribution of pixels throughout the images to be able to produce its own pictures of cats.

What is generative AI used for?

GAI already has use cases across many different industries such as manufacturing and health care. Some examples of how you can implement GAI include: 

• Manufacturing: In the manufacturing industry, GAI is capable of recognizing problems and suggesting solutions to keep equipment functioning. GAI can also advise you on supply chain decisions by analyzing availability, costs, and logistics.

• Health care: GAI is benefitting the health care industry by enhancing the quality of medical images to improve the accuracy of diagnosis. It also allows health care providers to see how certain treatments may impact a patient's health based on the data.

• Insurance: For insurance companies, GAI helps automate administrative work. It is also capable of detecting fraud and assessing risk by analyzing data and recognizing patterns.

• Automotive: In the automotive industry, GAI can play a valuable role in the design process by helping companies design cars more efficiently. This allows the auto companies to move towards production faster while also contributing to more innovative, creative car designs.

Advantages of generative AI

Choosing to implement GAI systems can offer your organization several advantages. For businesses, the ability to automate routine processes can lead to significant increases in productivity by freeing employees up to focus on other work. Additionally, GAI can improve customer service by providing access to chatbots at any hour of the day. It also can help develop more personalized experiences and product offerings. 

Disadvantages of generative AI

Although GAI certainly has its benefits, some challenges and potential downsides exist as well. One such challenge is the ethical concerns raised by GAI. In some cases, people may use GAI to create fake content and misinform the public. Also, GAI models may contain sensitive data, making it important to take the extra steps of encrypting data and following data privacy regulations. 

Large language models

An LLM is another type of artificial intelligence powered by deep learning that’s capable of generating human language, allowing the model to write text, translate different languages, answer questions, and perform other tasks relating to language. LLMs work by analyzing massive amounts of training data and learning to predict what word will come next in a sentence by assigning words a probability score based on the surrounding context. Eventually, LLMs gain the knowledge to pick up on these patterns accurately and generate relevant text.

What are large language models used for?

LLMs have a range of useful business applications for industries such as marketing and computer programming. Examples of real-world use cases for LLMs include:

• Marketing: Developing marketing campaigns that consist of sending content through avenues such as social media and emails is simpler with the help of LLM text generation. LLMs can generate relevant responses by analyzing documents directly from your business, as well as web content. It can also allow you to organize your customer base into segments in order to take a more personalized approach through sentiment analysis using natural language processing. 

• Computer programming: LLMs make coding easier for programmers by not only writing parts of code for you, but by debugging, increasing efficiency in code writing, and even translating code between programming languages.

• Education: LLMs offer benefits for teachers, making it possible to quickly take care of routine tasks such as creating lesson plans and grading quizzes. This frees up time for teachers to focus on more meaningful tasks.

Advantages of large language models

A significant advantage of utilizing LLMs is the time-saving aspect since you can automate several different processes. The flexibility of LLMs is another benefit: You can train them to perform a wide range of tasks. They can provide innovative solutions to problems, offering a new perspective on challenges. 

Disadvantages of large language models

When working with LLMs, it’s important to consider some of their challenges. For example, in order to use LLMs effectively and generate usable results, you need to know how to create the correct prompts. Additionally, the quality of your LLM output can vary depending on the training data it receives and how you’re using it.

Generative AI vs. large language models: Other things to consider

Since the term “GAI” is used to describe artificial intelligence models that generate content, LLMs are technically a type of GAI because you can use LLMs to generate text. 

The GAI industry projects substantial growth over the next decade as businesses use the tool to operate more efficiently, minimize costs, and better serve their customers. Data from Statista suggests the global GAI market will reach $356 billion by 2030 [1].

Discover more about GAI and LLMs on Coursera

GAI and LLMs are forms of artificial intelligence capable of generating text. GAI goes further than LLMs with its ability to generate images, videos, and more. Generative AI Fundamentals Specialization from IBM on Coursera covers various applications of GAI, with projects to help you practice generating text and images.

Generative AI with Large Language Models from DeepLearning.AI is another great option to grow your knowledge of generative AI, explore the architecture of LLMs, and discover practical applications for LLMs.