Generative AI vs Large Language Models: What’s the Difference

Generative AI vs LLM is a massive topic in the development world right now. But how do they differ, how do they compare, what can they offer in the real world, and which is better?

It’s safe to say that artificial intelligence (AI) is everywhere right now. AI in real estate, transportation, healthcare and content creation, among many other areas. This means that AI product development and, by extension, AI development services are now more important than ever across all industries.

But with all the talk in the media right now around generative AI (genAI), how does this technology differ from large language models (LLMs)? What are the common applications of generative AI? In what ways are large language models used in technology today, and what are the technological foundations of generative AI and LLMs?

The following blog will strive to answer these questions and outline the differences, discuss potential benefits and explore which solution might be better when it comes to implementing generative AI vs LLM into any organization. However, before diving into any specifics, let’s get our definitions straight.

ut with all the talk in the media right now around generative AI (genAI), how does this technology differ from large language models (LLMs)? What are the common applications of generative AI? In what ways are large language models used in technology today, and what are the technological foundations of generative AI and LLMs?

The following blog will strive to answer these questions and outline the differences, discuss potential benefits and explore which solution might be better when it comes to implementing generative AI vs LLM into any organization. However, before diving into any specifics, let’s get our definitions straight.

Read also: LLM Model Comparison Guide

What is generative AI?

GenAI does exactly what it says on the tin. It’s an AI smart enough to generate its own content by analyzing essays or even writing them from scratch. It can also generate images or even pieces of music when given precise instructions on what users want.

With this in mind, it’s important to note that this technology – while revolutionary – is not yet ready to produce any content on the level of Shakespeare, Van Gough or Mozart and still requires a human in the loop to ensure the content it produces is still to the standard expected from professional writers, artists or composers.

This, in a nutshell, is what genAI is and what it can do, but what are LLMs and how do they fit into the generative AI vs LLM debate?

What are large language models?

In short, LLMs are a subset of genAI which can be used to produce text from scratch. But only text – LLMs cannot produce images and pieces of music like genAI can.

Imagine genAI as a creative master in Renaissance times who works with several promising apprentices to sharpen their skills in specific fields. Now imagine LLMs as working under this Renaissance master as a young playwright or author, and you get a better idea of their relationship. GenAI can help bolster the intelligence of LLMs but at the end of the day, LLMs are just one subset of genAI.

With this mentor/mentee dynamic in mind then, how do both systems compare when businesses consider the benefits of generative AI vs LLM and which technology better suits their organizational needs?

Generative AI vs LLM – comparison

This article has already outlined how generative AI encompasses technologies capable of creating varied content types, such as images and music. In contrast, LLMs are specialized for text-based applications, focusing on tasks like natural language understanding, text generation, language translation and textual analysis. But what other differences are at play here and what other factors do organizations need to keep in mind when comparing both technologies?

In short, genAI focuses on the relationships between data, enabling it to create content from scratch, while LLMs are more focused on learning to interpret texts better so that they can process more complicated texts faster as time goes on. This means that the main difference between the two technologies, aside from the content they can produce, is that generative AI doesn’t need anything to base its content on, while LLMs need a document to interpret before they can do anything.

Generative AI and LLMs – use cases

There are many different use cases that could be discussed in this blog. However, for the purposes of brevity, this article will only focus on a few key examples of potential applications to illustrate the opportunities both generative AI and LLMs offer across industries. These use cases include leveraging both technologies to:

• Produce high-quality marketing materials: GenAI can produce unique images, music and text, while LLMs can produce sharper texts at speed. All these skills naturally feed into improving marketing operations for any organization.
• Help clinicians better diagnose patients: GenAI can analyze X-rays, for example, to help clinicians better understand what is wrong with patients and take the appropriate steps towards treatment. Meanwhile, LLMs can be used to track results during clinical trials, enabling medical teams to see correlations and trends much faster than more traditional analytical methods.
• Ensure finance professionals detect fraud and organize portfolios faster: Through GenAI’s and LLMs’ ability to analyze patterns, bankers can recognize suspicious finance requests faster. They can also use this technology to group materials together in their clients’ portfolios, which leads to customers having more trust in the banks they work with and feeling more secure about their financial future.
• Help developers code faster: Due to the same technology that supports finance professionals in detecting fraud and organizing portfolios at speed, genAI and LLMs can also help developers notice connections in code much faster, ensuring it works better overall. Additionally, through GenAI’s ability to create content from scratch, it can also write code to some extent, which speeds up developers’ work and enables them to focus on more business-critical tasks for their customers.

These examples are just the beginning when it comes to genAI vs LLM but there’s one more question that needs answering here: which solution is better? Surprisingly, the answer isn’t as straightforward as you might think.

Which AI solution is better?

When organizations are comparing generative AI vs LLM technology, it’s not really a question of which is better. It’s a case of which best suits your needs. If you only deal with text in your daily roles, for example, by writing blogs, technical manuals or brochures, then LLMs are right for you.

However, if your organization is a truly creative business that works on projects that include key visuals, social media flash cards, bid videos, blogs or web banners, to name a few, then a wider genAI solution would better suit your needs.

But even if you know already what technology works better for you, the question remains, “Where do you start?” At Software Mind, we know that implementing genAI or an LLM takes both a strategic approach and specialized skillsets, which can make AI-driven projects seem daunting and difficult to start for some businesses.

That is where our experienced software experts come in. They can help choose and deploy the right technology for you quickly and easily by connecting with you and understanding more about what you need these technologies for, which in turn will save you significant costs in time and resources. To learn more about how our experienced AI experts can help you drive your growth goals, reach out via this form.