Artificial Intelligence (AI) has become an integral part of our digital landscape, revolutionizing industries and reshaping how we interact with technology. In recent years, we've witnessed the emergence of powerful AI tools, with large language models (LLMs) and generative AI at the forefront of this technological wave.

This article aims to demystify and distinguish between these two prominent AI technologies: LLMs and generative AI. While both fall under the umbrella of artificial intelligence, they serve different purposes and excel in distinct areas. 

LLMs are ideal for understanding and generating human-like text, making them perfect for tasks like content creation and language translation. Generative AI, on the other hand, shines in producing new, original content across various mediums, including images, music, and even 3D models.

What are large language models (LLMs)?

LLMs are advanced AI agents that understand and generate human-like textual responses. They are trained on diverse datasets and billions of parameters to learn the complexities of human language. 

With deep learning techniques, specifically transformer architectures, LLMs process and produce contextually relevant and coherent text. When you give a prompt, they predict and generate the next words based on their training. This capability allows them to perform various natural language processing (NLP) tasks like translation, summaries, sentiment analysis, and question-answering. 

Some of the most widely used LLMs worldwide include: 

What is generative AI (genAI)?

Generative AI refers to a class of artificial intelligence systems that can create new, original content based on training data and user inputs. It closely imitates human-generated content. These AI models are designed to generate various types of data, including text, images, audio, video, and even 3D models. The key characteristic of generative AI is its ability to produce content that is novel, and not simply a reproduction of existing data.

At a high level, generative AI operates through a sophisticated process that begins with extensive training. The AI model is fed large datasets relevant to the type of content it will generate. For instance, an image generation model might be trained on millions of images. During this training phase, the model learns to recognize patterns, structures, and relationships within the data.

As the model processes this vast amount of information, it develops a probabilistic understanding of how different elements in the data relate to each other. This allows the AI to grasp the underlying structure and characteristics of the content it's learning from.

When given a prompt or input, the model leverages its learned patterns and probabilities to generate new content. This content is statistically similar to its training data but unique in its composition. The generation process involves the AI making numerous decisions about what elements to include and how to combine them, based on its training.

Many generative AI systems employ advanced techniques to refine their outputs. These may include adversarial training, where the model competes against itself to improve, or incorporating human feedback to enhance the quality and coherence of the generated content.

The result is an AI system capable of producing original content that can range from realistic images based on text descriptions to complete musical compositions or even computer code. This process demonstrates the power of generative AI to not just analyze existing data, but to create something entirely new.

Some of the most widely used generative AI models worldwide include: 

Learn how data.world used GenAI to enrich Snowflake’s metadata.

Comparing LLMs and Gen AI

Large Language Models (LLMs) and Generative AI are closely related technologies within the broader field of artificial intelligence. In fact, LLMs can be considered a specific type of generative AI focused on language tasks. However, there are significant differences in their scope, applications, and underlying technologies. Let's compare these two AI paradigms across several key areas to better understand their unique characteristics and capabilities.

Core functionalities

LLMs give textual responses like a human brain would after analyzing extensive information. They can handle content generation tasks, which has helped the marketing industry since GPT was made public.

However, GenAI creates new responses based on its learning across various modalities. Its functionalities aren’t limited to text—Gen AI models can even generate images, music, and other forms of creative output.

Model architecture

While LLMs usually serve as foundational frameworks for GenAI, their model architecture is quite different. They are based on transformer models, which use self-attention mechanisms to understand and generate text. For example, GPT-3 and BERT consist of multiple layers of neural networks that produce coherent and contextually relevant text. 

Compared to LLMs, GenAI models use a variety of architectures depending on the type of content they are generating. For example, Generative Adversarial Networks (GANs) are commonly used for image generation, while Variational Autoencoders (VAEs) generate diverse data types.

Underlying techniques

LLMs primarily rely on techniques like self-attention and unsupervised learning from large text corpora. They often use methods like masked language modeling or next-token prediction during training.

Generative AI employs a wider range of techniques. In addition to those used in LLMs, it might incorporate adversarial training (as in GANs), variational inference (as in VAEs), or diffusion models. The specific techniques depend on the type of content being generated and the desired properties of the output.

Applications

LLMs are predominantly used in natural language processing tasks. This includes chatbots, content creation, code generation, and language translation. They're also valuable for text analysis, sentiment analysis, and information retrieval.

Generative AI has a broader range of applications. It's used in creative fields for generating art, music, and video. In product design, it can create new product concepts or 3D models. In scientific research, it can generate molecular structures for drug discovery. The applications extend to any field where novel content creation is valuable.

Output quality and control

The quality of output for any LLM depends on its training data. While they generate contextually relevant responses, a model can sometimes produce incorrect information. 

This phenomenon is called hallucination and usually occurs when LLM provides nonsensical information due to its limited dataset. But you can now solve this issue using a knowledge graph-based data catalog in LLMs. Such catalogs filter data to provide contextually rich results.

GenAI’s output accuracy depends more on the model’s architecture and training data. 93% of the people believe that a data strategy is important to get valuable results from GenAI. This is because biased or incorrect information in the training datasets can lead to hallucinations, where the AI generates incorrect responses.

Simply put, both LLMs and generative AI models require careful tuning and validation to ensure high-quality and reliable outputs.

Scalability and performance

LLMs are known for their ability to scale to extremely large sizes, with models like GPT-3 and GPT-4 containing hundreds of billions of parameters. This scaling often correlates with improved performance across a wide range of language tasks.

Generative AI models vary in their scalability. Some, like large diffusion models for image generation, benefit from increased scale. Others may focus more on algorithmic improvements or specialized architectures for specific types of generation. The relationship between scale and performance in generative AI can be more complex and task-dependent compared to LLMs.

Choosing the right tool: LLMs vs. Generative AI

Selecting the appropriate AI technology for your needs is crucial for achieving optimal results. Here's a decision-making framework to help you choose between LLMs and genAI based on your specific requirements:

Type of content you want to generate (text vs. other formats)

The primary consideration in your decision should be the type of content you want to generate:

Text-based content: If your project primarily involves text generation, analysis, or manipulation, an LLM is likely your best choice. LLMs excel at tasks such as: 

If your purpose extends beyond mere text and you want high-level images or videos, then you should choose a GenAI model. It's ideal for: 

Desired level of creativity

If you're seeking structured and factual formats, choose LLMs. They're generally better suited for tasks that require adherence to specific formats, factual accuracy, or logical consistency. They're excellent for:

If you're seeking highly creative and novel formats, choose genAI. Generative AI often excels at producing more diverse and unexpected outputs, especially in non-textual domains. Choose Generative AI for:

Specific functionalities required

If you need deep language comprehension, context awareness, or the ability to engage in nuanced text-based tasks, an LLM is your best bet. This is particularly useful for: 

If you need to transform one type of content into another or generate content based on multi-modal inputs, genAI is more suitable. This is ideal for: 

Consider which technology offers the level of control you need: 

Decision Matrix

To summarize, here's a quick decision matrix.

Choose LLMs if you need:

Choose genAI if you need:

Remember, these are general guidelines. In some cases, you might benefit from using both technologies in combination, leveraging the strengths of each to create more comprehensive and powerful AI solutions.

The power of knowledge graphs for LLM and GenAI

Knowledge graphs serve as a powerful tool that enhances the capabilities of both LLMs and genAI. 

Knowledge graphs consist of nodes representing entities, connected by edges that define their relationships. This structure allows for a rich, nuanced representation of information that goes beyond simple facts to capture complex contexts and connections. By integrating knowledge graphs, AI systems can significantly improve their context understanding, reasoning abilities, and overall performance.

For LLMs, knowledge graphs provide a factual backbone that enhances accuracy in question-answering tasks and maintains consistency in longer text generations. They help bridge the gap between the vast amount of unstructured data these models are trained on and the structured, relational understanding required for more human-like reasoning.

In the context of genAI knowledge graphs inform the creation process across various modalities. Whether generating images, audio, or multimodal content, these structured knowledge representations ensure that the AI's outputs align more closely with real-world logic and relationships.

The synergy between AI models and knowledge graphs represents a significant advancement towards more intelligent and versatile AI systems. Here’s why you should build your LLMs and genAI models using a knowledge-graph-based data catalog:

data.world's AI advantage: Powered by a knowledge graph

Effective data governance is imperative to maintain data quality and security. For this purpose, knowledge graphs organize and connect data with a structured approach and create a rich semantic layer for AI models. This helps AI models easily understand the data context and produce 3x more accurate responses.

data.world is the only data catalog platform that uses knowledge graphs to deliver AI-ready data for your LLMs or GenAI models. This architecture can provide your organization with maximum data quality through the following features:

Book a demo with data.world today and learn how we can transform your data management and take your organization to new levels of efficiency.

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