Data & AI Evangelist
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As artificial intelligence continues to reshape industries and drive innovation, two terms have increasingly surfaced in discussions—generative AI and large language models (LLMs). These concepts are often used interchangeably, causing confusion among technologists, businesses, and the public. While both contribute significantly to the AI landscape, they are distinct in scope and capabilities.
The primary misunderstanding stems from the fact that LLMs, such as GPT-4 or BERT, are often considered the face of generative AI. In reality, LLMs are a subset of the broader field of generative AI, designed to understand and generate human language. On the other hand, Generative AI is an umbrella term encompassing various models and techniques used to create new content—text, images, music, or even software code.
The frequent comparison between LLM and generative AI is not only misleading but fundamentally flawed. LLMs drive some generative AI applications, but equating the two is incorrect, as generative AI can do far more than what LLMs specialize in. Through this blog, we aim to clarify the difference between LLMs and Generative AI, dispelling common misconceptions and providing a clearer understanding of how each contributes to the future of AI.
Read the full story: How is Gen-AI reshaping the retail employee experience?
What is generative AI?
Generative AI, or gen AI, is artificial intelligence focused on producing original content, such as audio, text, images, coding, and video—based on a massive volume of trained data. The generative AI models respond to users’ prompts or requests by identifying patterns, contexts, and relationships in their training data to formulate output that mirrors human creativity. Some of the best generative AI tools are DALL-E, Midjourney, Google’s Gemini, and ChatGPT by OpenAI.
Generative AI models use machine learning algorithms to learn patterns in large amounts of data and generate realistic content that mimics those patterns. Business leaders recognize generative AI’s massive positive impact in improving their internal workflow and streamlining tasks to remain competitive and drive growth. Considering its widespread influence, 80% of organizations will use generative AI applications and generative AI application programming interfaces (APIs) by 2026, reported Gartner.
Generative AI models rely on foundation models – deep learning models to create highly realistic content on demand, each of which is designed to excel at specific generative tasks. The foundation models in generative AI include:
- Variational Autoencoders (VAEs)
Variational autoencoders are foundation models that generate new data samples based on the data they are trained on, resulting in data samples similar to the original input data.
- Large Language models (LLMs)
The large language model is a common foundation model for text generation and their application extends beyond mere content generation—they also excel in language understanding.
- Generative adversarial networks (GANs)
Generative adversarial networks are foundation models that are most commonly used for generating images and videos.
Further reading: 7 important steps to begin your generative AI journey.
What are large language models (LLM)?
Large language models (LLMs) are deep learning models designed to understand, generate, and work with human language. Built on massive datasets and leveraging deep learning techniques, particularly transformer architectures, LLMs can process natural language, making them central to many AI-driven applications like chatbots, virtual assistants, and automated content generation tools. Some top large language models (text-generation models) are BERT, Gemini, Cohere, ChatGPT, and Claude LLM.
The term “large” refers to the immense size of these models in terms of both the volume of training data and the number of parameters to capture and represent language patterns. LLMs learn linguistic structures, context, semantics, and syntax in human language by training on massive datasets and using deep learning techniques. This allows them to generate coherent and contextually relevant text based on the user’s prompts.
Large language models vs Generative AI: Key differences to understand
Artificial intelligence has come a long way, and terms like large language models (LLMs) and generative AI are often tossed around as if they’re the same thing, which can be confusing. While both technologies are important in advancing data and AI, they have distinct roles and work differently.
Understanding the key differences between LLMs and generative AI will help clarify how they fit into the bigger picture of AI and why these distinctions really matter for the future of technology. Let’s take a closer look at what sets LLMs (Large Language Models) vs. Generative AI apart:
| Aspect | LLM (Large Language Model) | Generative AI |
| Definition | A type of AI model trained to understand and generate human-like text based on vast datasets. | AI systems designed to create new content, such as text, images, or music, from learned data. |
| Focus | Primarily focused on text-based tasks like answering questions, translating, or summarizing. | Encompasses a broad range of media (text, images, audio, video, etc.), generating entirely new content. |
| Key Functionality | Understanding context and generating human-like text responses. | Creating new, original data or content based on patterns learned from training data. |
| Training Data | Trained on vast amounts of text data (e.g., books, articles, websites). | Trained on varied data sources, such as text, images, or sound, depending on the application. |
| Applications | Chatbots, translation services, content summarization, and code generation. | Image generation (e.g., DALL-E), text generation, music composition, and video creation. |
| Examples | GPT models, BERT, RoBERTa | DALL-E, MidJourney, DeepFake, GPT for text generation. |
| Strengths | Excellent at understanding and producing coherent text. | Ability to generate creative and high-quality content across various media. |
| Weaknesses | Limited to text; cannot generate non-text content. | Needs large datasets and significant computational power to generate accurate results. |
| Use Cases | Answering questions, generating conversational agents, and summarizing documents. | Creating artwork, generating synthetic data, automating creative processes. |
LLM and Generative AI: Redefining the AI landscape with limitless applications
Generative AI and LLM are transforming how we work across various industries and disciplines with immense potential. By leveraging a colossal amount of trained data, these technologies are creating and understanding human language surprisingly intuitively, making our interactions with technology much more human-like. As these technologies continue to evolve, large language models and generative AI open exciting new avenues for creativity and innovation.
Let’s explore how large language models and generative AI are impacting:
Content generation
Generative AI: Generative AI uses machine learning algorithms to produce new content, such as text, images, music, or video, based on patterns learned from existing data. For instance, tools like ChatGPT can generate articles, stories, and marketing copy, offering unique outputs that can save time and enhance productivity.
LLMs: Large language models, such as OpenAI’s GPT-3 or Google’s BERT, are trained on vast datasets, enabling them to understand context, tone, and style. LLMs have the potential to produce high-quality, coherent text that aligns with user prompts, making them invaluable for generating reports, blog posts, and even creative writing.
Chatbots and virtual assistants
Generative AI: Generative AI enables more coherent responses in chatbots and virtual assistants by creating natural and intuitive responses. Gen AI also provides diverse response options, improving the user experience.
LLMs: LLMs are the backbone of most chatbots and virtual assistants. They are trained to understand natural language and produce human-like interactions using conversational capabilities.
Personalized content
Generative AI: Gen AI can analyze user preferences and behavior to create tailored content experiences. Leveraging user data helps generate personalized marketing campaigns that resonate more deeply with individual users, thereby enhancing engagement.
LLMs: LLMs facilitate personalization by processing and understanding user inputs at a granular level. They adapt their responses based on user history, preferences, and context. For example, in e-commerce, an LLM helps create personalized product descriptions or recommendations based on user searches and purchases.
Coding
Generative AI: Generative AI expands into coding through models that can write, debug, or optimize code across multiple programming languages. Tools like GitHub Copilot are early examples of how AI assists developers by providing coding suggestions in real-time, significantly improving developer productivity and reducing repetitive tasks.
LLMs: LLMs play a significant role in code generation and debugging by understanding programming languages and contexts. They generate code snippets based on natural language descriptions, assist in writing documentation, and offer solutions to common coding problems.
Learn more: The potential impact of generative AI in the retail industry.
Debugging common myths around LLM vs Generative AI
With all the buzz around large language models (LLMs) and generative AI, it’s no surprise that users are starting to mix them up. Let’s break down some of the common myths floating around and clear up what these technologies are all about in a simple way.
Myth 1: All LLMs fall under the umbrella of generative AI, and all generative AI tools are LLMs.
Generative AI is a broad term that covers any type of AI that can create new content, like text, images, or even videos. These tools are powered by different AI models, one of which is large language models (LLMs) specifically designed for generating text.
But generative AI isn’t just about text. It can also include models that work with videos, images, or audio. So, while LLMs are great for text, generative AI as a whole is much more versatile. So, all LLMs fall under the umbrella of generative AI, but not all generative AI tools are LLMs.
Myth 2: Generative AI is basically predictive AI.
The difference between predictive AI and generative AI is based on their core functions. Although both generative AI and predictive AI come under AI, both serve different purposes. Predictive AI focuses on analyzing historical data to make predictions about future events. Generative AI, on the other hand, focuses on creating new content based on trained data.
In the debate of generative AI vs predictive AI, predictive AI tells you what’s likely to happen, whereas generative AI gives you something entirely new from scratch. Both are important, but they operate in distinct ways. So, generative AI isn’t predictive AI.
Myth 3: LLMs can be used for both text and image generation
This is a common misconception. Large language models (LLMs) are designed to generate and understand text, not images. While there are other generative AI multimodal models that can create both text and images, such as DALL-E.
LLMs themselves are built to work exclusively with language. Their strength lies in generating coherent text, answering questions, or even holding conversations, but when it comes to image generation, that’s a task for other types of AI models. So, LLMs can create text-only outputs.
Confiz | Your partner in kicking off a successful generative AI journey
As we look toward the future of artificial intelligence, it’s important to recognize the unique contributions of both Large Language Models (LLMs) and generative AI. Understanding what is LLM helps clarify its role in processing and generating human language, while generative AI encompasses a much broader scope, producing everything from text to images, music, and beyond. These technologies, while interconnected, serve distinct purposes within the AI ecosystem.
Looking to create your generative AI model for your business use case? Confiz possesses widespread expertise in transforming your business workflows with a Gen AI virtual assistant built on Azure OpenAI services. Our generative AI virtual assistant combines conversational AI with powerful generative AI features to understand natural language and provide human-like responses trained using your business data. Contact us at marketing@confiz.com and explore the possibilities of your use case across various industries with our gen AI experts.
