Generative AI vs Large Language Models: Key Differences and When to Use

9 min read
Generative AI vs Large Language Models: Key Differences and When to Use
Generative AI vs Large Language Models

LLMs are just one category of generative AI, and not all gen AI tools rely on LLMs. Still, these two concepts get many people confused, especially non-technical ones. Understanding what differentiates gen AI from LLMs is crucial. For example, if your project requires natural language processing and text generation, you must understand LLMs' capabilities and limitations. On the other hand, if your project is focused on image generation, music composition, or similar creative tasks, generative AI is your go-to.

So overall, you need to understand the difference between gen AI and LLM to make an informed decision about technology adoption.

Generative AI vs Large Language Models
Generative AI vs large language models

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 gen 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!

The definition and examples of generative AI and LLMs

Let’s kick things off by defining generative AI and LLM and taking a look at some examples. We will start with generative AI, as it is a broader concept.

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 is a super-popular generative AI platform designed to generate images based on the user's prompts, or inputs. Simply put, you describe what image you want to create, and DALL-E will get the job done. The secret behind this technology is large language models, which we will talk about later, that help the platform understand the meaning behind users’ inputs.
  • AIVA is another great example of gen AI that helps with creating music. By analyzing patterns and structures in existing music, AIVA applies this data to come up with new musical compositions and lyrics without involving humans.
  • Midjourney is pretty similar to DALL-E, but this solution specializes in creating lifelike virtual characters that can be used in various applications, including video games, virtual reality apps, and advertisements. What sets Midjourney apart from others is its ability to create highly realistic and detailed facial images. Users can customize the generated faces by adjusting features, such as hair color, facial expressions, age, and gender. The system works by using deep learning techniques. Midjourney trains its model on an extensive dataset of images with human faces. Once trained, the model can generate new, 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 ML algorithms in our portfolio.

Case in point: transforming images with gen AI filters

Developed by Flyaps, our solution boosts users’ creativity through AI-powered filters. Our team of AI 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.

Flyaps' generative AI solution for transforming images
Flyaps' generative AI solution for transforming images

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 has 175 billion parameters and can translate languages, summarize text, and help with writing. This model has encouraged many developers to test its potential for coding tasks and is now used in various projects. The famous GitHub Copilot was notably inspired by GPT-3. Currently, the model is updated to GPT-3.5 – a more flexible version that can understand and generate more sophisticated texts.
  • GPT-4 was released in 2023 and for now, it is the biggest model in the GPT series. It has more than 1.76 trillion parameters and can handle both language and images. However, GPT-5 is coming soon.
  • BERT stands for bidirectional encoder representations from transformers. This open-source model aims to enhance the understanding of human language by predicting missing words in sentences. The term “bidirectional” is crucial here, as it means the model considers both the words preceding and following a given word to grasp its meaning. This approach enables BERT to capture the complete context of a sentence and make more precise predictions about the words that may follow. Accessible to all, organizations use BERT for various tasks like document classification or biomedical text mining.

So, LLM can be roughly called one of the generative AI models or its key component. Gen AI can rely on it (as well as on other models) to analyze and understand text-based data.

The key differences between LLM and other generative AI models

Now that we have a basic understanding of what generative AI models and LLMs are, let's delve a bit deeper into the topic. Here is what sets both technologies apart when talking about their implementation.

Large language models

Other generative AI models


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 generative AI and LLM across different industries

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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.


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


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.


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.

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.


LLMs can be used by teachers who want to provide more tailored learning experiences to students. The models can quickly adapt lesson plans, provide personalized feedback, and monitor student progress in real-time.

With features like speech-to-text and text-to-speech, generative AI systems make education more accessible for students with disabilities. Moreover, AI-driven avatars make video lessons more fun and cheaper than traditional ones.


LLM is often used for creating personalized training materials for new employees or for developing chatbots that would answer all common questions newcomers usually ask.

By using special gen-AI-based systems to analyze data from sensors and other sources in real-time, some manufacturers create accurate digital twins of their products or processes. These copies are used for testing and predicting issues before they appear in real life. The technology also can track inventory and manage 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

Gen AI can create all sorts of content. However, to do this, it often needs clear instructions from users. Take DALL-E, for instance, where users must describe the image they want. This is why generative AI and LLM become such a powerful combo. LLMs are great at understanding context and meaning in multiple languages, interpreting user input more effectively.

If you’re looking to develop a solution powered by gen AI or LLM, you have to team up with a skilled software development company, like Flyaps. Given our extensive experience with all kinds of AI models, we are the best advisors when it comes to choosing between generative AI and LLM. So, let go of deciding - drop us a line and we will take care of all the technical issues for you.