Compare generative AI vs. LLMs: Differences and use cases

Key differences between generative AI and 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.

1. Applications

LLMs primarily produce text output. Common use cases for LLMs include the following:

• Text generation. LLMs can produce coherent, context-aware text based on a user's input, from marketing collaterals to fiction passages to software code.
• Translation. LLMs can translate text from one language to another, although they typically fare worse than purpose-built translation models and struggle with less common languages.
• Question answering. Although their ability to provide factual answers is limited, LLMs can explain concepts by simplifying terminology or using analogies, offer advice on specific topics and answer many natural-language questions.
• Summarization. LLMs can summarize and identify key arguments in lengthy passages of text. For example, Google's Gemini 1.5 Pro can analyze a million tokens in one go -- equivalent to roughly 750,000 words or nine average-length novels.
• Dialogue. LLMs can simulate conversation by providing responses in a back-and-forth dialogue, making them ideal for chatbots and virtual assistants.

Generative AI, in contrast, is a much broader category. Its common use cases include the following:

• Image generation. Models like Midjourney and Dall-E can produce images based on users' textual prompts. Some, such as Adobe Firefly, can also edit portions of human-created images -- for example, generating a new background for a portrait.
• Video generation. A newer category in the generative AI landscape, models like OpenAI's Sora can generate realistic or animated video clips based on users' prompts.
• Audio generation. These models can generate music, speech and other types of audio. For example, Eleven Labs' voice generator can produce spoken audio from users' textual input, and Google's Lyria model can generate instrumental and vocal music.
• Data synthesis. Generative models can create artificial data that mimics -- and can be used in place of -- real-world data. While synthetic data can present problems if relied on too heavily, it's useful for training ML models when real data is hard to come by or particularly sensitive. For example, a team training a medical model could use synthetic data to avoid or minimize the use of personal health information.

2. Architecture

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.

3. Training data

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.

4. Challenges and limitations

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.

Main use cases for generative AI

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:

• Compile research.
• Create marketing and promotional images.
• Improve fraud detection.
• Optimize supply chains.
• Personalize output for users.
• Summarize meeting notes.
• Translate language.

Examples of 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:

• Versatile chatbots, such as OpenAI's ChatGPT and Google Gemini (formerly Bard).
• Image generators, such as Midjourney and OpenAI's Dall-E.
• Code generation tools, such as GitHub Copilot and Amazon CodeWhisperer.
• Audio generation tools, such as AudioPalm and Microsoft Vall-E.

Main use cases for LLMs

LLMs create humanlike interactions by comprehending and mimicking natural language. Consequently, they have many use cases for organizations, including the following:

• Content classification.
• Conversational chatbots.
• Rephrasing text.
• Sentiment analysis.
• Summarization.
• Translation.
• Text generation.

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.

Examples of LLMs

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:

• Google's Palm and Gemini models.
• Meta's Llama series of open source models.
• OpenAI's GPT series, including GPT-4o and GPT-4.
• Anthropic's Claude series, including Sonnet, Opus and Haiku.

The history of generative AI and LLMs

The current popularity of generative AI and LLMs is relatively new. Both technologies have evolved significantly over time.

Generative AI types

The category generative AI encompasses several types of ML algorithms. The following are some of the most common:

• Generative adversarial networks. Introduced in 2014, GANs are ML models in which two neural networks compete. The first network -- the generator -- creates original data, while the second --- the discriminator -- receives data and labels it as either AI-generated or real. By employing deep learning methods and a feedback loop that penalizes the discriminator for each mistake, the GAN learns how to generate increasingly realistic content.
• Variational autoencoders. Also introduced in 2014, VAEs use neural networks to encode and decode data, enabling them to learn techniques for generating new data. The encoder compresses data into a condensed representation, and the decoder then uses this condensed form to reconstruct the input data. In this way, encoding helps the AI represent data more efficiently, and decoding helps it develop more efficient ways of generating data. VAEs can complete a variety of content generation tasks.
• Diffusion models. Introduced in 2015, diffusion models are popular for image generation. These models work by gradually adding noise to input data over several steps to create a random noise distribution, then reversing this process to generate new data samples from that noise. Many image generation services, such as Dall-E and Midjourney, apply diffusion techniques and other ML algorithms to create highly detailed outputs.
• Transformers. Introduced in 2017 to improve language translation, transformers revolutionized the field of NLP by using self-attention mechanisms. These mechanisms enable transformers to process large volumes of unlabeled text to find patterns and relationships among words or subwords in the data set. Transformers opened the door for large-scale generative AI models, especially LLMs, many of which rely on transformers to generate contextually relevant text.
• Neural radiance fields. Introduced in 2020, NeRFs employ ML and artificial neural networks to generate 3D content from 2D images. By analyzing 2D images of a scene from various angles, NeRFs can infer the scene's 3D structure, enabling them to produce photorealistic 3D content. NeRFs show potential to advance multiple fields, such as robotics and virtual reality.

The LLM evolution

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.

Future of generative AI and LLMs for businesses

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.