What are the differences and similarities between GenAI and LLMs?

Artificial Intelligence has become an umbrella term for various technologies like RAGs, LLMs, GenAI, and AI automation. While they all run on AI at their core, they differ in terms of practical application and training data. Similarly, both GenAI and LLMs are distinct AI models, but have so many commonalities that some users struggle to tell them apart. To find the right application for them in a business environment, it’s important to understand the differences between the two.

What is GenAI?

GenAI, or a Generative AI model, is a type of artificial intelligence that can create new, unique content based on users' prompts. The type of content can vary; for example, it could be a text, an image, a video, a programming code, or a 3d model.

Examples of GenAI

New GenAI models appear constantly. Currently, each type of content has at least a few solid alternatives.

• Midjourney, DALL-E 3, or Stable Diffusion (images)
• Sora and Synthesia (videos)
• Meshy (3D models)
• Cursor and GitHub Copilot (code)
• Suni (music)

And what are LLMs?

LLMs, or Large Language Models, are a type of artificial intelligence that generates content based on the user’s prompt. However, LLMs are focused only on text. They can generate longer and shorter pieces of writing, mimic a conversation, or answer a question.

It is worth remembering that LLMs differ in terms of capability, and generally, the more parameters a model has, the "smarter" it is. For example, DeepSeek was trained on a large dataset, as indicated by its size of 685 billion parameters, whereas Bloom has just 176 billion parameters. The size of the training data is also a factor —the larger the model, the more data it was trained on.

Examples of LLMs

At the time of writing this article, there are almost 1.7 million different AI models available. A large portion of that figure is made up of LLMs, both commercial and open-source.

Even with such a variety of LLMs, only a few have gained popularity over the last few years, partially thanks to their connection to the largest players in tech.

• ChatGPT versions GPT-4o, GPT-4o mini or GPT-3.5. (from OpenAI)
• Gemini 1.5 Pro and Flash models (from Google)
• Anthropic’s Claude 3.5 Sonnet and the Claude 3 Opus (from Anthropic)
• Llama 3.1 (from Meta)

GenAI vs. LLM: The differences and similarities between them

In short, GenAI and LLMs are different in terms of the content they generate, how they are trained, and what they can be used for. They also come with their own risks and requirements.
 

Differences in the type of content it generates

Depending on the model, GenAI can generate images, videos, music, and text. Content can be mixed; for example, you ask GenAI to tell a story about an image or to generate a video based on a text input. LLMs are all about generating text, but they can also create code.

Differences in the data that each model is trained on

Different types of GenAI models require different types of input. For example, Sora was trained on YouTube, Facebook, and Instagram videos. LLMs are trained with text-based data, such as books, websites, chats, etc.

Problems with AI

Despite the huge and in many ways positive impact that the ongoing AI revolution has on our lives, it creates new problems and challenges. For example, it is utilized to spread misinformation or to plagiarize academic and artistic work. Also, current models tend to hallucinate answers that have no basis in real life.

• Academic plagiarism: Academic fraud is an ongoing issue with LLMs, as some students submit essays and final papers generated with AI instead of doing their own work.
• Artistic plagiarism: Most GenAIs use elements of existing works to generate new images. If you ask an AI model to generate an image of a painting, the chances are that some elements of that painting might be a direct copy from another painting that was used as training data. There is an ongoing debate on whether this counts as plagiarism. The argument is that if a human painter used elements of someone else's work, we would have called it so. Unfortunately, with AI, the lines are blurred, and it’s difficult to protect the artists and their original work.
• Hallucination: AI models can hallucinate and generate answers that sound plausible, but are not based on facts. For example, it can answer a question by providing details of a non-existent product, such as iPhone 17. The degree of hallucination is measured with the Hughes Hallucination Evaluation Model (HHEM). At the time of writing this article, the HHEM for the best models varies between 0.7% and 2%, but for others it can reach as high as 10%.

• Misinformation: When used irresponsibly, hallucinations of AI models lead to the spreading of misinformation. For example, when a website, a newspaper, or even a company blog publishes AI-generated content without checking the information, they would unknowingly give more legitimacy to that false information.

  Also, there is a large concern about deepfakes created with GenAI models that can generate images and videos. In the political world, deepfakes have become a dangerous weapon used to spread misinformation about opposing parties.

• Regional bias: Since AI models are trained primarily on English-language data, their answers may reflect the perspective and text formatting of countries from the Western hemisphere and perform differently depending on location.
• Nondeterministic behavior: All AI models are probability-based, which means their answers can vary, even when the same question is asked multiple times.
• Lack of memory: Models do not retain memory of previous interactions and treat each query independently.
• Context window limitations: There is a limit to how much data a model can process in a single query, which can restrict the depth and detail of its responses.

How LLMs and GenAI are used

We are still at the stage of discovering new commercial applications, but generally, GenAI models are used in marketing to create content, and they help programmers write code faster or help data scientists visualize data. There are tools, like GitHub's Copilot, that are created with one specific use case in mind. Others, such as Gemini, are flexible in the sense that they can be applied to different use cases. 

LLMs are also a leading force behind the renewed interest in no-code and low-code development, caused by existing solutions leveraging AI-powered features. It’s a great option for small projects as it improves their workflow processes; however has limitations in terms of scalability and stability.

With LLMs like ChatGPT, we already see companies using AI-powered customer service, journalists writing headlines and pieces of articles, and business professionals translating emails and documents in foreign languages.

Similarities between GenAI and LLMs

Since LLMs are a type of GenAI model, there will be common points between them. The similarities are in the technology that powers them, in how they are trained, and in their applications. There are also common points and ethical questions that they raise.

• Underlying technology: Both use advanced machine learning, especially transformer architectures, to understand and generate data.
• Training on large datasets: Both require large datasets; GenAI uses multimedia data, and LLMs focus on extensive text corpora.
• Applications across industries: Both LLMs and GenAI are versatile and are used in fields like healthcare, e-commerce, education, and entertainment for tasks such as content creation, support, and detection.
• Ethical challenges: Both face issues like data bias, copyright concerns, misinformation, and misuse (e.g., deepfakes or academic dishonesty).

Possible implementations of LLMs in different industries

At VirtusLab, we have seen great results from an internal LLM chatbot taking the role of a company-wide search tool. We believe that this could become a common practice in the future. But even now, there are countless applications for LLMs across industries.

LLM in the financial industry

A financial institution specializes in a process of risk assessment and decision-making to protect the financial institution from losses. The institution partnered with VirtusLab to improve its verification process of potential clients. The current method required manual data entry, which was time-consuming.

Our engineers developed a proof of concept using LLM agents and the LangChain framework. This solution included 3 components: decision-making, task execution, and process orchestration.

The new AI-powered system researches each prospective client in multiple online sources and generates a concise overview. This enhanced the accuracy of the verification process while cutting research time from 20 minutes to about 1 minute per client.

LLM in the tech industry

Another example is an AI-powered email writing tool that we built for a technology firm that specializes in gathering data for lead generation.

A leading business intelligence provider faced challenges in scaling personalized customer outreach due to inefficient communication methods and limited in-house expertise in machine learning. Their current method, as email-based outreach, was time-consuming and often ineffective due to generic messaging.

VirtusLab implemented an intelligent email generation solution powered by an LLM agent. This system enabled real-time data integration, personalized message creation, and A/B testing. Our solution gave the client the ability to adjust the tone and length of the messages and draw information from the most recent company data.

Implementing this writing tool reduced the time to compose each email to under 5 seconds. Also, it improved email response rates and increased daily requests across the client’s B2B platform.

GenAI vs. LLM - a conclusion

GenAI and LLMs are so different that there is no definitive way to tell which model is better. The choice of the right one depends on the business requirements of each use case.

However, there are cases where neither GenAI nor LLMs should be considered. For example, if the data you are working on is highly sensitive, you need a classic Machine Learning approach, where data specialists prepare and analyze the data before drawing any conclusions. At its current stage, no AI comes close to the quality of business insights that classic Machine Learning provides.