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

Large language models (LLMs) help to build generative artificial intelligence (AI) applications. The key difference between the two is that generative AI focuses on generating new content based on its training data, while LLMs concentrate on learning from and interpreting data to generate reliable text outputs. As a developer, you’ll train LLMs on massive amounts of data which the model interprets and analyzes to help generative AI applications create content and respond to user queries and prompts similarly to a human. Explore the fundamentals of LLMs and generative AI, discover their key applications, and learn more about their potential advantages and disadvantages. 

Large language models (LLMs)

LLMs are deep learning models that you train using vast amounts of data, and once trained, they can interpret text inputs and generate credible text outputs. When you build LLMs, you’ll use machine learning (ML) transformer models, which are a type of neural network. These models are capable of learning without human intervention, and they can help you answer questions, summarize information, translate languages, and more. 

What are LLMs used for?

LLMs focus on developing text-based content and implementing natural language processing (NLP) techniques to generate human-like responses for chatbots and virtual agents. You’ll implement various steps to create your LLM before deployment, including: 

• Task specification: Specify what task your LLM will perform and set clear objectives. 

• Select model: Create your own model or use an existing model.

• Model adoption: Implement prompt engineering, finetuning, and feedback.

• Model evaluation: Consistently evaluate your model to develop optimal outputs.

• Model deployment: After optimizing the model, you can deploy it.

Some examples of LLMs include OpenAI’s GPT-3.5 and GPT-4 which are models used to implement ChatGPT tasks and generate text outputs. 

Advantages of LLMs

Some advantages of using LLMs include: 

• Proficient text generation: You can use LLMs to quickly analyze and interpret data to generate reliable text outputs in response to questions and prompts. 

• Continuous improvements: LLMs are constantly learning and implementing model training as they process new data, consistently improving their performance. 

• Flexibility: LLMs are versatile and flexible due to their vast amount of capabilities, including summarization, translation, and text generation. 

Disadvantages of LLMs

Some disadvantages that come along with using LLMs include: 

• Hallucinations: If you train LLMs on false or inaccurate information, they can produce hallucinations. Hallucinations occur when LLMs develop false outputs, disseminating incorrect information to users. 

• Security risks: Some users may share confidential, personal information with LLMs and this information may be exposed in response to other users if prompted. 

• Scaling difficulties: Maintaining and scaling LLMs can be intensive and challenging since they’re trained on vast amounts of data. 

Generative AI

Generative AI is a form of artificial intelligence that utilizes deep learning models to generate high-quality content, such as images and videos. Generative AI applications utilize the data they’re trained on to develop new content. According to Mark Murphy, head of US enterprise software research at J.P. Morgan, “Generative AI is the most important technological development of the last several decades. It is rapidly enabling use cases and scenarios that people once said would be impossible to achieve, and it’s only going to get smarter” [1]. 

What is generative AI used for?

Generative AI applications are capable of producing various forms of content, including text, images, video, audio, code, and various other forms of media. Generative AI applications consist of various architectures, including generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs are capable of generating realistic images and videos through a user's text prompt. VAEs are models you can use to create new data based on the model’s training data, such as images and a variety of other AI applications. ChatGPT, Gemini for Google Cloud, and DALL-E 2 are all examples of generative AI applications that you can use to create various forms of content. 

Advantages of generative AI

Some advantages of using generative AI include: 

• Creativity innovation: Generative AI applications can help you brainstorm and develop new ideas quickly and efficiently. 

• Applications in multiple fields: Generative AI can be utilized in a variety of topics and industries, including AI development, marketing, and graphic design. 

• Unique and diverse outputs: Generative AI can produce various outputs, including characters, environments, artwork, presentations, imagery, and more. 

Disadvantages of generative AI

Some disadvantages of using generative AI include: 

• Lack of credibility: The data and information produced by generative AI applications are hard to validate and confirm credibility due to their inability to verify their outputs. 

• Outdated information: Developers often train generative AI models with data that has cutoff dates, which means the model may provide outdated information if not updated in a timely manner. 

• Risk of plagiarism: Generative AI applications often paraphrase and repeat information from various websites without providing sources, leading to potential copyright infringement. 

LLM vs. Generative AI: Other things to consider

All LLMs are generative AI, but not all generative AI are LLMs. As a developer or user, it’s important to understand that generative AI and LLMs both generate new content based on their training data; however, generative AI has a broader scope of capabilities. For example, generative AI applications are capable of creating a wide variety of content, such as videos, images, and audio, enabling you to generate diverse forms of media. On the other hand, LLMs solely generate text, making them ideal for tasks like developing dialogue for chatbots and virtual agents. If you’re working with these models, you’ll focus more on text-based interactions. Depending on your project requirements, you may need to choose between these two or combine their capabilities to implement a broader range of applications. 

Discover more about LLMs and generative AI with Coursera

LLMs and generative AI share various similarities; however, they have some key differences, including their content generation capabilities and their applications. Expand your understanding of the fundamentals of LLMs and generative AI on Coursera with AWS’ Generative AI with Large Language Models.