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

What are the differences between generative AI and large language models (LLMs)? How are these two technologies related? The terms are often used interchangeably, but the distinction matters for enterprises deciding where AI fits in its operations. Knowing what each technology does well helps you choose the right tool for each job and connect those tools to the work your business runs on every day.

In this article, we'll break down how generative AI and LLMs differ, where they overlap, and how enterprises put them to work across their processes.

Here's the short version of generative AI vs. LLMs: 

• Generative AI is a form of artificial intelligence that creates new text, images, video, audio, or other content based on the vast amounts of data that the generative model was trained on.

• Large language models (LLMs) are a form of AI that focuses on understanding text inputs (using natural language processing) and creating human-like text based on a given input. LLMs are a subset of generative AI and focus primarily on language-related tasks.

This chart outlines the key differences:

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

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 LLMs, which were designed to generate text.  

However, generative AI also includes other types of AI—such as models that work with video, text, or audio inputs and outputs. While many are built on LLMs, some generative AI tools are trained on things like a series of videos to create realistic-looking video content. LLMs focus mostly on text, but generative AI as a technology category is broader.

LLMs used to only be able to accept text inputs. But now, with the development of multimodal LLMs, they can accept audio, imagery, etc. as inputs. For example, OpenAI also uses voice chats to understand human speech and read answers using voice text. 

Both generative AI and LLMs have revolutionized industries, but have done so in different ways. Generative AI, such as Midjourney, DALL-E 4 and Pika Labs, have changed the way we generate images and video, or created voice assistants and other audio. LLMs have focused more on text-based content creation but still have other significant uses.

LLMs have been around since the early 2010s, and are rapidly evolving to core infrastructure in products and enterprises, with several key shifts defining 2026. They’ve evolved from text-only outputs to multimodal tools, and now agentic AI is transforming how enterprises operate.

AI agents can handle activities such as analyzing data, routing work, updating systems, and completing process steps. They can even act autonomously to perform tasks and meet goals without requiring human input.  

In summary, what’s the difference between LLMs and generative AI? An LLM is a type of AI model that uses machine learning trained on extensive amounts of data to understand and produce text-based work product, while 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 other formats of new content.

So when should you apply each form of AI? 

LLMs are being used for a wide range of text-based tasks, such as language translation, content generation, and content personalization. They are also being used to power document analysis and processing, AI-assisted development, and agentic AI. These include code-generation tools for building new apps or generating answers from critical business documents.  

Generative AI models can include these tasks, but can be broadened for other creative fields like image generation, music composition, or video creation. However, it's critical to note that generative AI still uses the LLM for their text-based tasks.While these two technologies have consumer uses, they also have numerous business applications that are helping enterprises across a range of tasks. 

At Appian, we see AI delivering the most value to enterprises when it is applied across processes end to end, rather than as isolated tools.  By deploying generative AI in critical business processes, you can offload repetitive tasks to AI, freeing up people for higher-value work and increasing work efficiency.