Generative AI vs LLM: What is Best For Your Business?

Not all generative AI tools are developed on LLMs, but all large language models are a form of gen AI.

LLMs are a subset of generative AI that focuses specifically on text generation. Generative AI, in general, can create various outputs, including text, images, audio, and code. While LLMs are designed for language related tasks like drafting emails or summarizing documents, generative AI has broader content generation capabilities. When you interact with an AI system and receive a language-based response that feels human-like, there is a good chance that an LLM is responsible for it.

Although both the technologies are becoming integrated into our daily interactions. Yet, many people may misinterpret the characteristics that define these technologies and how they operate.

This blog will walk you through a deeper understanding of LLMs and other generative AI tools.

  Generate  Key Takeaways Generating...

• Large Language Models use transformer-based technology to process language, while Generative AI utilizes various methods like CNNs, diffusion models, and GANs to create outputs in different forms, including images, audio, and video.

• LLMs focus on generating and understanding text, whereas Generative AI takes it a step further by producing images, videos, music, and even synthetic data.

• If you need tools for text automation, chatbots, or document workflows, LLMs are the best choice. For creative tasks in design, marketing, or product innovation across multiple media, Generative AI offers more value.

• LLMs are trained on large text datasets to ensure accuracy in language. In contrast, Generative AI works with multi-modal data, allowing for more creativity and richer experiences for users in various fields.

• LLMs boost productivity, communication, and automation, while Generative AI supports innovation, creativity, and personalization. Together, they set the stage for the next generation of AI advancements.

Generative AI vs LLM: A Detailed Comparison for Business Leaders

Generative AI is a broader category of AI that creates new content. Large Language Models are a specific type of generative AI that focuses on understanding and producing text. LLMs are just one part of generative AI, which can also create images, audio, code, and videos. The table below explains the difference between LLM and generative AI.

Now, that we have already got a glimpse over the differences between generative AI and LLMs, let us move forward with exploring the differences in detail.

1. Architectural Difference 

The AI algorithms, natural language processing and deep learning techniques that power large language models are different from those used in other generative AI models.

Most LLMs are built using transformers. Transformers use attention mechanisms, which help them understand long text passages by connecting words and showing their importance. While transformers are common in LLMs, they can also be found in other generative AI models, like those that create images.

On the other hand, some architectures used in generative AI for non-language tasks are not used in LLMs. A notable example is convolutional neural networks (CNNs). CNNs are focused on image processing and are great at identifying features in images, such as edges, textures, and even entire objects and scenes.

2. Functional Differences 

LLMs specialize in language generation, text-based tasks, utilizing advanced neural networks to understand and generate human language, ensuring accurate responses . In contrast, generative AI encompasses a broader range, creating content in various media formats, including text, visuals, and sounds.

3. Output Nature and Quality Expected

The outputs from large language models and generative AI show their specific strengths. LLMs create text that is clear and accurate, which is essential for making human-like interactions.

Generative AI is skilled at creating different types of content, such as an image generation model, and is known for its ability to innovate. Some examples of this innovation include,

• The AI-generated artwork 'Portrait of Edmond de Belamy' received significant attention in the art world.

• AI-generated music explores new styles.

• AI-generated poetry introduces new themes and forms.

These examples illustrate how generative AI promotes creativity across various fields.

4. Training Data

Training data and model design are closely connected. The type of data used to train a model influences the choice of algorithm.

Large Language Models are trained on extensive sets of text data. This data comes from a variety of sources, including novels, news articles, and Reddit posts. In contrast, other generative AI models may use different types of data, like videos, images, or audio files, based on their purpose.

Due to these differences in data types, the training process varies for LLMs and other generative AI models. For instance, preparing data for an LLM involves different steps compared to preparing data for an image generator. The amount of training data can also vary: an LLM requires a broad range of data to understand human language effectively, while a more specialized generative model needs a focused set of data.

5. Application Domains of LLM vs Generative AI for Businesses

The areas where large language models and generative AI are used are diverse and growing. LLMs assist with tasks that require a deep understanding of language, like running chatbots or translating text. Generative AI, on the other hand, works well in creative fields by producing art, music, and synthetic media. In fact, The range of generative AI services has expanded, including chatbots like ChatGPT and Anthropic's Claude. There are also tools for creating content, such as Midjourney and DALL-E for images, and video creators like Runway ML.

6. Real-world applications: LLMs vs Generative AI

Large language models mainly create text. Here are some common LLM use cases in business:

• Text Creation: LLMs can write clear and relevant text based on what you ask for.
• Translation: LLMs can convert text from one language to another. However, they are generally not as good as models specifically designed for translation and may struggle with less common languages.
• Answering Questions: LLMs have some limitations in providing accurate facts. Still, they can explain ideas in simpler terms, use analogies, give advice on various topics, and respond to many types of questions in natural language.
• Summarizing: LLMs can condense long texts and highlight main arguments. For instance, Google’s Gemini 1.5 Pro can process around a million tokens at once, which is about 750,000 words or nine average-length novels.
• Dialogue: LLMs can mimic conversation by responding in a back-and-forth manner, making them useful for chatbots and virtual assistants.

Generative AI is a broad category that includes several key uses:

• Image Generation: Gen AI tools like Midjourney and Dall-E can create images based on text prompts.
• Video Generation: This is a newer area in generative AI. Models like OpenAI's Sora can create realistic or animated video clips from text prompts.
• Audio Generation: These gen AI models can make music, speech, and other types of audio. For example, Eleven Labs' voice generator can turn text into spoken audio.
• Data Synthesis: Generative AI models can produce artificial data that mimics real-world data. Excessive use of synthetic data can be problematic, but it's useful for training machine learning models when real data is scarce or sensitive.

• Some popular examples of generative AI models include AI chatbots like OpenAI's ChatGPT, image generation tools such as Midjourney, and code generation tools like GitHub Copilot and Amazon CodeWhisperer, which help you write computer code more easily.

Quick Insight for Business Leaders

If your goal is language automation, document generation, or conversational AI, LLMs are your best fit.
But if your business needs multi-format content creation, design innovation, or synthetic data generation, a broader Generative AI approach is more valuable.

Future of Generative AI and LLMs for Businesses

The AI market is changing quickly, with new large language models (LLMs) and generative AI tools coming out almost every day.

Many new generative AI tools now have multimodal capabilities, meaning they can work with different types of data. This blurs the line between LLMs and other types of generative AI.

Multimodal generative models build on traditional LLMs by understanding other data types. Instead of just handling text, these models can also work with images and audio. For example, users can now upload images to ChatGPT, which can include those images in its text conversations.

Another important change is the rise of agentic AI, which refers to autonomous agents that can achieve goals and complete tasks on their own. Companies are starting to add agentic AI features to their generative AI products. These AI agents not only understand and respond to user requests but can also perform actions like operating a computer or making a purchase. The goal of these AI agents is to improve efficiency.

Bottom Line

As Generative AI and Large Language Models continue to develop, it's important to understand their differences for better decision-making in innovation. Generative AI allows organizations to create in various formats, such as text and images. While, LLMs are designed to understand and process natural language. Together, they make a strong system that can change how businesses work, innovate, and engage with customers.

For businesses seeking to use these technologies effectively, having the right AI development expertise is crucial. Our AI development services guide you in designing custom LLMs and implementing robust generative solutions, helping you move from trying out ideas to achieving real results.