LLM vs Generative AI: What’s the Difference?

Generative AI and Large Language Models (LLMs) are both powerful technologies, but they’re often misunderstood. LLMs are a subset of Generative AI, meaning that all LLMs are part of the broader Generative AI family, but not all Generative AI is limited to language. 

While LLMs focus on understanding and generating text, Generative AI can create content across various formats like text, images, and videos.

In this blog, we’ll explain the difference between these two technologies and when to use each one. 

What is Generative AI?

Generative AI is a type of artificial intelligence technology that can produce various types of content including text, imagery, audio, and synthetic data. Generative AI creates new and original ideas by learning patterns from data.

Generative AI is a subset of machine learning. It uses artificial neural networks that can process both labeled and unlabeled data. It works through supervised, semi-supervised, and unsupervised learning methods to generate new content.

What is LLM?

Large Language Models (LLMs) are advanced machine-learning models designed to understand and generate human language. They are based on transformer architecture, a groundbreaking neural network system introduced by Google. 

LLMs are a subset of Generative AI. What sets LLMs apart is their ability to process and analyze language on a massive scale, making them capable of understanding context, nuances, and complex language patterns.

The term “large” refers to both the size of the neural networks and the vast datasets used to train these models. LLMs are trained on trillions of tokens—pieces of text—from diverse sources like books, articles, websites, and more. 

This extensive training allows them to perform a wide range of language tasks, such as answering questions, summarizing content, writing essays, and even generating code. 

For example, ChatGPT, powered by an LLM, can carry out conversational tasks, explain technical concepts, and draft creative writing with remarkable accuracy and fluency.

Key Differences Between Generative AI and LLMs

Definition 

Generative AI is a comprehensive field of artificial intelligence that focuses on creating original content across various formats, including text, images, videos, and even 3D models. 

Large Language Models (LLMs), such as OpenAI’s GPT-4 and Google’s PaLM, are a subset of generative AI. 

They specialize in processing and generating text, excelling in tasks like summarization, translation, and answering questions. 

While all LLMs fall under the umbrella of generative AI, not all generative AI models are LLMs.

Scope and Application

Generative AI has a wide range of applications that extend beyond text. For instance, it’s used in generating synthetic voices for virtual assistants, creating personalized video content, or even crafting unique designs for fashion and architecture. 

In contrast, LLMs are specialized tools within the text domain. Applications like customer support chatbots, email drafting, and document summarization are where LLMs excel. 

Gartner reports that by 2026, more than 80% of global enterprises will adopt generative AI tools in some capacity.

Training Data and Modalities

Generative AI models are trained on datasets that often include a mix of text, images, audio, and video. For example, models like DALL-E learn from annotated image-text pairs to create visually coherent outputs. 

On the other hand, LLMs are trained on textual data only. This narrow focus allows them to excel in understanding linguistic nuances, idiomatic expressions, and even cultural contexts. 

OpenAI’s GPT-3, for instance, was trained on datasets comprising over 300 billion words from diverse sources like Wikipedia and literary works.

Technological Framework

Generative AI integrates various model architectures, including:

GANs (Generative Adversarial Networks): Used in applications like deepfake creation and image synthesis.

VAEs (Variational Autoencoders): Primarily used for generating high-quality images or compressing data.

Transformers: Powering text generation, coding assistance, and multimodal content generation.

LLMs rely almost exclusively on transformer architectures, which use self-attention mechanisms to process and generate text. The introduction of transformers has been pivotal for advancements in LLMs, enabling contextually rich and coherent outputs.

Customizability & Fine-Tuning

Generative AI often demands specialized fine-tuning, especially for domain-specific applications like medical imaging or legal document synthesis. 

In contrast, fine-tuning LLMs is relatively straightforward and typically involves training on a smaller dataset tailored to specific text-based tasks. 

For example, a healthcare organization could fine-tune an LLM to handle patient queries by using its own medical FAQs.

Performance Metrics

Generative AI: Evaluated based on the realism, diversity, and creativity of the output. For example, AI-generated images are assessed for visual coherence and originality.

LLMs: Measured for textual accuracy, fluency, and contextual relevance. For instance, an LLM’s ability to generate human-like conversation or summarize a complex article determines its efficacy.

According to Accenture, organizations that leverage generative AI for creative tasks report a 20-30% increase in productivity.

Examples in Action

Generative AI

DALL-E: Generates realistic images from textual prompts.

Runway ML: Offers tools for video editing and AI-generated special effects.

Synthesia: Produces AI-generated video content with synthetic avatars.

LLMs

GPT-4: Powers chatbots, language translation tools, and creative writing applications.

BERT: Helps search engines like Google better understand user intent for more accurate search results.

T5 (Text-to-Text Transfer Transformer): Converts tasks like translation, summarization, and Q&A into text-to-text formats for streamlined problem-solving.

Generalized & Technical Usability

Generative AI appeals to both technical and non-technical users. For instance, graphic designers can use tools like Canvas AI-powered features without deep technical knowledge. Meanwhile, developers can integrate generative models into applications using APIs or SDKs. 

LLMs, while accessible via interfaces like ChatGPT, are also heavily used by developers to create NLP-based solutions. Stats report that startups leveraging LLMs for SaaS solutions have seen a 40% increase in customer acquisition rates.

Ethical Considerations

Ethical concerns are paramount for both technologies. Generative AI often faces scrutiny for potential misuse in creating deepfakes or generating misleading content. 

Similarly, LLMs are criticized for biases embedded in their training data. Both require robust guidelines and transparency to ensure responsible use.

Generative AI vs Large Language Models: When to Use Each

So, when should you turn to Generative AI or Large Language Models (LLMs)? While these two AI technologies overlap in capabilities, their ideal applications often depend on the nature of the task and your business goals. Here’s a breakdown to help you decide:

When to Use LLMs

LLMs are a versatile solution for text-based applications that require high accuracy, context understanding, and efficient processing. They excel in tasks such as:

• Language Translation: Translating content across multiple languages with contextual accuracy.
• Content Personalization: Crafting tailored email responses or marketing campaigns.
• Customer Service Automation: Powering chatbots that handle customer queries in real-time, reducing response time and improving satisfaction.
• Example: REVE Chat’s LLM-based chatbot delivers seamless multilingual support and helps automate customer conversations while maintaining a human touch.
• Knowledge Management: Summarizing vast amounts of text data, such as legal documents or case files, into actionable insights.
• AI Copilots: Assisting with code generation or document analysis, enabling faster workflows for developers and professionals.

LLMs shine in environments where precise text generation and in-depth data interpretation are paramount.

When to Use Generative AI

Generative AI is your go-to for creative and multimedia-focused tasks. While it can perform text-based operations (often relying on LLMs for this), it also extends into visual, audio, and even interactive content creation. Some ideal use cases include:

• Image and Video Generation: Designing branded visuals, product mockups, or explainer videos.
• Music Composition: Generating custom soundtracks or audio assets.
• Marketing Campaigns: Creating synthetic personas, drafting creative taglines, and producing unique graphics for ads.
• Interactive Customer Support: Generating dynamic walkthrough videos or product tutorials that enhance customer engagement.
• Example: A financial services team could use Generative AI to create a video guide for customers struggling with complex account processes.
• Data Visualization: Converting raw data into visually engaging charts and infographics tailored to your brand’s style.
• Generative AI’s strength lies in its ability to create from scratch, making it invaluable for industries like marketing, design, and entertainment.

Combining Generative AI and LLMs for Business Success

The true power of AI emerges when Generative AI and LLMs are combined. Consider these examples:

Customer Support: An LLM-powered chatbot like REVE Chat can answer text-based customer queries, while Generative AI can complement this by creating personalized video tutorials or email responses.

Case Management: A case worker uses an LLM to summarize client details and a Generative AI tool to produce an engaging guide for complex processes.

Marketing Personas: Marketers prompt an LLM to gather behavioral insights about their audience and then use Generative AI to design visuals that bring personas to life.

Conclusion

It’s not about Generative AI vs. LLMs. It’s about leveraging the best of both worlds. While LLMs offer precision and depth for text-based tasks, Generative AI opens doors to creativity and multimedia applications. Together, they can transform industries, enabling businesses to scale operations, engage customers, and streamline workflows.

With solutions like REVE Chat’s LLM-based chatbot and Generative AI, you can take the first step in combining these technologies to deliver personalized customer experiences and drive business growth. Book a free demo now.

Frequently Asked Questions