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

Elizabeth Bell, Appian
December 21, 2023

What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. 

To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself. For example, it suggested, “Generative AI is the chatterbox at the cocktail party who keeps the conversation flowing with wild anecdotes, while LLMs are the meticulous librarians cataloging every word ever spoken at every party.” I mean, who sounds more fun? Well, joke’s on you, ChatGPT, because without LLMs, you wouldn’t exist. 

Text-generating AI tools like ChatGPT and LLMs are inextricably connected. LLMs have grown in size exponentially over the past few years, and they fuel generative AI by providing the data they need. In fact, we would have nothing like ChatGPT without data and the models to process it. 

Here, you can explore another common question: Generative AI vs. machine learning.

3 facts about generative AI vs. LLMs.

Three major things stand out when you compare generative AI and LLMs. 

1. Not all generative AI tools are built on LLMs, but all LLMs are a form of generative AI.

Generative AI is a broad category for a type of AI, referring to any artificial intelligence that can create original content. Generative AI tools are built on underlying AI models, such as a large language model (LLM). LLMs are the text-generating part of generative AI. 

2. LLMs create text-only outputs.

LLMs can only create text outputs, and they used to only be able to accept text inputs, as well. When OpenAI first released ChatGPT in 2022, it was built on a text-only LLM, GPT-3. But now, with the development of “multimodal” LLMs, these LLMs can accept audio, imagery, etc. as inputs. OpenAI’s next iteration, GPT-4, is an example of a multimodal LLM. 

Both generative AI and LLMs will revolutionize industries, but they will do so in different ways. Generative AI could change the way we do 3D modeling, generate video output, or create voice assistants and other audio. LLMs will focus more on text-based content creation but still have other significant uses (and may play a role in wider generative AI options like voice assistants).

3. LLMs are only growing.

LLMs have been around since the early 2010s, but they gained popularity when powerful generative AI tools like ChatGPT and Google’s Bard launched. Everest Group notes that one reason 2023 saw such exponential growth is the expansion of parameters in large language models, with GPT-4 having more than 175 billion parameters.

This chart outlines the key differences.

In summary, what’s the difference between LLMs and generative AI? Generative AI is a category that contains a myriad of tools built to use information from LLMs and other types of AI models using machine learning to generate new content, while an LLM is a type of AI model that uses machine learning built on billions of parameters to understand and produce text. 

So is ChatGPT right about the chatterbox vs. the librarian? I hate to admit it, but perhaps it is. 

To flesh out this concept further, we’ll look at some examples showing the interplay between generative AI and LLMs. 

Throughout this piece, you’ll notice that we’ve described data and AI as intertwined. That’s because they are—inextricably. So when you think about operationalizing AI at your organization, start with the most fundamental piece: data privacy. Our guide to private AI explains where to begin with data privacy and AI.

Examples of LLMs and GenAI at work.

Take a look at the three examples below to understand how LLMs and other forms of generative AI play different roles.

Case management.

A customer asks a case worker a question about their case. Rather than going through each email, document, and chat transcript to find an answer, the case worker asks a large language model to provide a summary of data related to the question. The LLM provides a textual overview of the key players, case highlights, and suggested next steps. In this scenario, the customer was also having technical trouble uploading documents to their case, so the case worker uses a generative AI-powered video creation tool to send them a video walkthrough of the process.

Marketing persona creation.

A marketer wants to create a synthetic audience persona via generative AI. They prompt an LLM with questions like “Where does my persona get their news?” or “How does my persona like to be communicated with?” and use the responses to craft a story about their persona. Once done, they take that information and prompt a generative AI tool to create images that represent that persona. 

Data analysis and visualization.

An analyst takes a data file and uploads it to an LLM. They ask the tool to analyze the data and provide trends. The analyst vets the trends and uses their knowledge of the data’s context to select and edit only the trends that make sense. They then use a generative AI tool to create charts that display the trend data in their organization’s brand colors. 

As you can see, generative AI is a big, broad category that includes multiple models—LLM is one of those that’s gotten a lot of attention (and LLMs are certainly versatile), but they’re just one type of generative AI. 

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