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For many people, the phrase generative AI brings to mind large language models such as OpenAI’s ChatGPT. Although LLMs are an important part of the generative AI landscape, they’re only one piece of the bigger picture.
LLMs are a type of generative AI specialized for linguistic tasks, such as text generation, question answering and summarization. Generative AI, a broader category, encompasses a much wider variety of model architectures and data types. In short, LLMs are a form of generative AI, but not all generative AI models are LLMs.
Generative AI models use machine learning (ML) algorithms to create new content based on patterns learned from their training data. For example, a generative AI model for creating new music would learn from a data set containing an extensive collection of music samples. The AI system could then create music based on user requests by employing ML techniques to recognize and replicate patterns in music data.
LLMs are a type of generative AI that deals specifically with text-based content. They use deep learning and natural language processing (NLP) to interpret text input and generate text output, such as song lyrics, social media blurbs, short stories and summaries. LLMs differ from other types of generative AI in their narrow focus on text over other data types and their typically transformer-based model architecture.
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LLMs primarily produce text output. Common use cases for LLMs include the following:
Generative AI, in contrast, is a much broader category. Its common use cases include the following:
The underlying algorithms used to build LLMs differ from those used for other generative AI models.
Most of today’s LLMs rely on transformers for their core architecture. Transformers’ use of attention mechanisms makes them well suited to understanding long text passages, as they can model the relationships among words and their relative importance. Notably, transformers aren’t unique to LLMs; they can also be used in other types of generative AI models, such as image generators.
However, some model architectures used for nonlanguage generative AI models aren’t used in LLMs. One noteworthy example is convolutional neural networks (CNNs), which are primarily used in image processing. CNNs specialize in analyzing images to discern notable features, from edges and textures to entire objects and scenes.
Training data and model architecture are closely linked, as the nature of a model’s training data affects the choice of algorithm.
As their name suggests, LLMs are trained on vast language data sets. The data used to train LLMs typically comes from a wide range of sources — from novels to news articles to Reddit posts — but ultimately, it’s all text. In contrast, training data for other generative AI models can vary widely and might include images, audio files or video clips, depending on the model’s purpose.
Due to these differences in data types, the training process differs for LLMs versus other types of generative AI. For example, the data preparation stages for an LLM and an image generator involve different preprocessing and normalization techniques. The scope of training data could also differ: An LLM’s data set should be comprehensive to ensure that it learns the fundamental patterns of human language, whereas a generative model with a narrow purpose would need a more targeted training set.
Training any generative AI model, including an LLM, entails certain challenges, including handling bias and acquiring sufficiently large data sets. However, LLMs also face some unique problems and limitations.
One significant challenge is the complexity of text compared with other types of data. Think about the range of human language available online: everything from dense technical writing to Elizabethan poetry to Instagram captions. That’s not to mention more basic language issues, such as learning to interpret an odd idiom or use a word with multiple context-dependent meanings. Even advanced LLMs sometimes struggle to grasp these subtleties, leading to hallucinations or inappropriate responses.
Another challenge is maintaining coherence over long stretches. Compared with other types of generative AI models, LLMs are often asked to analyze longer prompts and produce more complex responses. LLMs can generate high-quality short passages and understand concise prompts with relative ease, but the longer the input and desired output, the likelier the model is to struggle with logic and internal consistency.
This latter limitation is especially dangerous because hallucinations aren’t always as obvious with LLMs as with other types of generative AI; an LLM’s output can sound fluent and seem confident even when inaccurate. Users are likely to notice if an image generator produces a picture of a person with eight fingers on each hand or a coffee cup floating over a table, for instance, but they might not pick up on a factual error in an LLM’s well-written summary of a complex scientific concept they know little about.
Generative AI has a number of business benefits, including improving customer experience, automating repetitive tasks and helping develop new products or ideas. But for organizations to get ROI from generative AI, they must find the right use case.
The following are some examples of how organizations can use generative AI:
Choosing the right generative AI tool comes down to matching its capabilities with the organization’s objectives. The tool market is rapidly changing, but the following are a few popular examples:
LLMs create humanlike interactions by comprehending and mimicking natural language. Consequently, they have many use cases for organizations, including the following:
Newer multimodal models widen the scope of use cases, with models such as GPT-4o making it possible for an LLM-based chatbot to handle use cases like image generation.
LLMs belong to a class of AI models called foundation models. As the term suggests, LLMs form the fundamental architecture for many AI language comprehension and generation applications.
Examples of popular LLMs include the following:
The current popularity of generative AI and LLMs is relatively new. Both technologies have evolved significantly over time.
The category generative AI encompasses several types of ML algorithms. The following are some of the most common:
In 1966, the Eliza chatbot debuted at MIT. While not a modern language model, Eliza was an early example of NLP: The program engaged in dialogue with users by recognizing keywords in their natural-language input and choosing a reply from preprogrammed responses.
After the first AI winter — the period between 1974 and 1980 when AI funding lagged — the 1980s saw a resurgence of interest in NLP. Advancements in areas such as part-of-speech tagging and machine translation helped researchers better understand the structure of language, laying the groundwork for the development of small language models. Improvements in ML techniques, GPUs and other AI-related technology in the years that followed enabled developers to create more intricate language models that could handle more complex tasks.
With the 2010s came further exploration of generative AI models’ capabilities, with deep learning, GANs and transformers scaling the ability of generative AI — LLMs included — to analyze large amounts of training data and improve their content-creation abilities. By 2018, major tech companies had begun releasing transformer-based language models that could handle vast amounts of training data (therefore dubbed large language models).
Google’s Bert and OpenAI’s GPT-1 were among the first LLMs. In the years since, an LLM arms race ensued, with updates and new versions of LLMs rolling out nearly constantly since the public launch of ChatGPT in late 2022.
The AI market is crowded and fast-moving, with new LLMs and generative AI models introduced almost daily.
Multimodal capabilities are increasingly common in new generative AI tools. These models can work with multiple data types, blurring the lines between LLMs and other types of generative AI.
Multimodal generative models expand on the capabilities of traditional LLMs by adding the ability to understand other data types: Rather than solely handling text, multimodal models can also interpret and generate data formats such as images and audio. For example, users can now upload images to ChatGPT that the model can then incorporate into its text-based dialogues, as shown in the screenshot below.
Another major shift is the recent rise of agentic AI: autonomous agents that can pursue goals and complete tasks without human intervention. AI and software vendors are beginning to integrate agentic AI capabilities into their generative AI products, creating agents that are able to not only interpret and respond verbally to user requests but also take actions such as operating a computer or making a purchase. The aim of these agents is ultimately to increase efficiency, but these technologies remain in their early stages and consequently are often buggy or limited in scope.
Editor’s note: This article was originally published in 2024. Informa TechTarget Editorial updated the article in 2025 to improve readability and expand coverage.
Lev Craig covers AI and machine learning as the site editor for SearchEnterpriseAI. Craig graduated from Harvard University with a bachelor’s degree in English and has previously written about enterprise IT, software development and cybersecurity.
Olivia Wisbey is the associate site editor for SearchEnterpriseAI. Wisbey graduated from Colgate University with Bachelor of Arts degrees in English literature and political science and has experience covering AI, machine learning and software quality topics.
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