LLM vs Generative AI: Key Differences and When to Use

BY Natalia Palamarchuk May 6, 2024 9 min read

LLM vs Generative AI

Contents

Large language models (LLMs) are just one category of generative AI, and not all gen AI tools rely on LLMs. This distinction often confuses people, especially non-technical users. Understanding generative AI vs LLM is crucial. if your project involves natural language processing or text generation, knowing the capabilities and limitations of LLMs is essential. Conversely, if your focus is on tasks like image generation or music composition, generative AI is the better fit.

So overall, you need to understand the generative AI vs LLMs to make an informed decision about technology adoption.

LLM vs generative AI — *LLM vs Generative AI*

It’s been 11+ years since we at Flyaps started helping businesses from various industries, such as recruitment, logistics, public sector, and so on, implementing both generative AI and LLMs. From our experience, many business owners mistake gen AI for large language models or mix them. For this reason, we decided to step in and clear things out. Further, we will explain the main differences between generative AI vs large language models and tell you when you would want to use them in your project. Sounds interesting? Then keep reading!

Are LLMs generative AI? Definitions and examples

Let’s start by defining generative AI and LLMs with a few examples. Since generative AI is the broader concept, we’ll begin there.

What is generative AI?

Generative AI refers to a type of model capable of generating not only new original text content but also images, video, and audio content. Gen AI tools can be built on LLMs and trained on large datasets to better understand human input and provide more accurate results. But this isn’t always the case.

Here are a few popular examples of gen AI to better understand the capabilities of this technology.

DALL-E

DALL-E is a popular generative AI platform that creates images from user prompts. You describe the image you want, and DALL-E generates it. The technology relies on large language models, which help it interpret user input — a topic we’ll explore later.

AIVA

AIVA is a generative AI tool for creating music. It analyzes patterns and structures in existing music to compose new pieces and write lyrics without human involvement.

Midjourney

Midjourney, like DALL-E, generates lifelike virtual characters, but it specializes in creating realistic facial images for use in video games, virtual reality, and advertisements. What sets Midjourney apart is its ability to produce highly detailed and customizable faces, allowing users to adjust features like hair color, expressions, age, and gender. The system uses deep learning, training on a large dataset of human faces to generate synthetic faces that closely resemble real ones.

To further cement our understanding of gen AI, let's analyze one more example. This time, we will take one of our cases, as we have plenty of customizable AI-based solutions with advanced machine learning models and algorithms in our portfolio.

Case in point: transforming images with gen AI filters

Developed by Flyaps, our solution boosts users’ creativity through artificial intelligence-powered filters. Our team of artificial intelligence specialists started with a data collection, gathering data from various sources. This dataset was later used to develop a generative AI model able to detect hidden patterns and connections within the dataset. Through extensive training, we taught the model to emulate specific artistic techniques, for example, pencil drawing, enabling it to generate distinct and personalized artistic effects.

As a result, our generative AI technology allows users to modify existing images by adjusting their style, lighting, color, or shape while preserving their original elements. The solution comes with tools to enhance image resolution without sacrificing specific details, ensuring improved visual quality. Moreover, the AI-powered system can fill in image gaps, reconstruct backgrounds, and pixels, or even repair damaged or torn photos, ensuring seamless image restoration.

Now that we understand what generative AI is, let's shift the focus to LLMs.

What are LLMs?

Large language models, or LLMs, are a subset of generative AI focused specifically on analyzing and generating text-based data. These models are trained on a large dataset (that’s where their name comes from) and use deep learning techniques, which involve specialized neural networks called transformers. Transformers allow LLMs to interpret the texts they’re trained on and generate new ones, mimicking the tone and style.

Though LLM is a narrower concept than generative AI, its range of applications is pretty extensive, from DNA research and sentiment analysis to online search and chatbots. But we will talk more about this a bit later. For now, let’s look at some of the most interesting examples of large language models.

GPT-3

GPT-3 with 175 billion parameters, can translate languages, summarize text, and assist with writing. Its success has led many developers to explore its potential for coding tasks, inspiring projects like GitHub Copilot. The updated GPT-3.5 offers more flexibility and can generate more sophisticated texts.

GPT-4

GPT-4 was released in 2023 and it is currently the largest model in the GPT series, with over 1.76 trillion parameters. It can process both language and images, with GPT-5 on the horizon.

BERT

BERT, which stands for bidirectional encoder representations from transformers, is an open-source model designed to enhance language understanding by predicting missing words. Its bidirectional approach considers both preceding and following words to capture full context, making it more accurate for tasks like document classification and biomedical text mining.

So, large language models can be roughly called a key component of generative AI, helping it analyze and interpret text-based data.

Generative AI vs large language models — the key differences

Now that we know what generative AI models and LLMs are, let’s explore their differences in implementation.

	Large language models	Other generative AI models
Architecture	LLMs are based on transformer (deep learning) architectures, optimized for processing sequential data like text.	Gen AI models can be based on various architectures depending on the type of data they are programmed to generate. For instance, generative adversarial networks (GANs) can use a CycleGAN architecture that learns transformation between images of different styles. On the other hand, variational autoencoders (VAEs), which are also used for tasks such as creating realistic images, use encoder+decoder networks.
Training data and the logic of decision-making	LLMs are trained on tons of language data from the internet (news articles, novels, X, or Reddit posts). Then, the models learn to predict the next words based on what they have seen before. As a result, after lots of practice, these models identify what combination of words makes sense depending on the context, and what doesn’t.	Other gen AI models can be trained on diverse types of data like music or video, depending on specific goals. The logic of training also can vary. For example, GANs are trained using a mini-max game objective decision rule. This algorithm is often used in the game industry to find the optimal move for the user.
Model size	When LLM is used solely, it focuses on one area – text generation, making it thrifty regarding computational resources and storage.	Models that handle complex data (music, video, images) or tackle multiple tasks may require a lot more computational resources and storage than LLM.
Development expertise	Pre-trained LLMs are user-friendly and don’t require much time or effort from developers to use them for text-based tasks.	Some generative AI models are more challenging to develop and fine-tune due to factors like complex model architectures.

The application of gen AI vs LLM across different industries

While generative AI use cases are more frequently discussed, as they can assist with more tasks in general, LLMs also have many practical applications. So here’s how you can use both.

Generative AI vs LLM in healthcare

LLMs can be used to analyze cancer-progression patterns from computed tomography reports to predict metastatic disease in multiple organs.

On the other hand, image-focused generative AI models like GANs or VAEs are great for creating images that mimic real patient scans but denoised and enhanced. These images help radiologists and clinicians to be more accurate in assessments.

The usage of both LLM and gen AI models has also led to significant advancements in drug discovery. LLM is commonly used for tasks like summarizing academic papers or annotating (analyzing raw data sets to gain structural information) molecules and proteins, saving a lot of time for scientists. Meanwhile, other models of generative AI identify disease-related biomarkers and optimize clinical trial design.

Additionally, gen AI can be applied to create personalized treatment plans based on patient data and predict disease progression and treatment outcomes

LLMs and generative AI in finance

As a text-focused model, LLM examines text from sources like news and social media to help financial institutions understand market trends, sentiments, and customer behavior better. It also automates such tasks as document review.

As an alternative, gen AI models can be applied to analyze tons of market data to find patterns to improve trading strategies. The technology also makes loan decisions faster and more accurate by checking documents and analyzing risks.

Law

For this domain, LLMs are great for creating legal documents like contracts and wills by using templates and input data. They also can be used for suggesting edits in real-time based on client preferences.

Gen AI in the legal industry can assist in tasks like analyzing patents and trademarks, helping lawyers manage their clients' intellectual property more efficiently. The technology is also used for quick searches through case law, legislation, and other sources to provide relevant information for legal professionals, saving time and effort.

Customer service

Customer service agents can use LLM-powered chatbots to access instant answers about different products and policies. By analyzing ongoing customer conversations and understanding customer queries, this technology suggests relevant responses tailored to the context of the conversation. This can even be done regardless of language, as LLM-based systems excel at real-time translation, enabling seamless multilingual support.

Talking about generative AI models, they can effectively summarize customer interactions with agents, making it easier to understand how issues are typically solved. This helps businesses improve their problem-solving strategies and customer service overall.

Another manual task that gen AI now handles for many businesses is responses to reviews. Whether it's expressing gratitude for positive feedback or addressing complaints, AI-generated responses tailor the message to the content of the review.

Generative language models in content creation

LLMs like BERT and GPT have proved their effectiveness in semantic search, complementing traditional keyword-based search methods. While traditional search is still relevant, semantic search understands the context and connection behind keywords, improving search results.

Gen AI processes a lot of data to customize the content, helping marketers make personalized campaigns that lead to more conversions. These campaigns also include images generated with Midjourney or DALL-E 3.

Education

LLMs help teachers create personalized learning experiences, adapt lesson plans, offer feedback, and track student progress in real time.

Generative AI features like speech-to-text and text-to-speech make education more accessible for students with disabilities. AI-driven avatars also make video lessons more engaging and cost-effective compared to traditional methods.

Generative AI and LLM in manufacturing

LLMs are used to create personalized training materials for new employees and develop chatbots that answer common questions.

Manufacturers use generative AI systems to analyze real-time data from sensors and other sources, creating accurate digital twins of products or processes. These digital replicas help test and predict issues before they occur. The technology also tracks inventory and manages logistics in real time.

Art and entertainment

LLMs are often used to add subtitles for movies and TV shows, create video game scripts, and marketing materials. They can ensure accurate and culturally appropriate translations across different languages, helping products to go global.

Authors, playwrights, and screenwriters use LLMs for brainstorming ideas, as the technology can suggest plot twists, and help with dialogues or character development.

Generative AI is widely used for composing melodies, beats and special effects for films. In gaming, the technology helps with creating realistic NPC behaviors, generating game content, and providing personalized recommendations for players.

Final thoughts

Generative AI can create diverse content but often needs clear instructions. With DALL-E, for instance, users describe the desired image. This is where generative AI and large language models work well together. LLMs interpret context and meaning across languages, enhancing user input.

To build a solution using generative AI or LLM, partner with an experienced software development team like Flyaps. With expertise in artificial intelligence models, we’ll help you choose the right approach and manage the technical details. Contact us, and we’ll handle the rest.