Large language models (LLMs) vs generative AI: what’s the difference?

Generative AI and large language models (LLMs).

These two cutting-edge AI technologies sound like totally different, incomparable things. One’s ostensibly a type of AI focused on content creation, while the other is a class of models. If they sounded like they were comparable, it might have been more like generative-model AI vs. large-model AI, or maybe generative language models vs. large language models.

Not exactly apples to apples. More like apples to eggplants. But despite the fact that these two sound divergent, their respective use cases have plenty of similarities. Plus, they’re not mutually exclusive; they can effectively complement each other as copilots, and when they do, they can fly high. With their respective markets worth billions, these two phenomena are charting a promising landscape in the future of healthcare, ecommerce (e.g., Amazon), real estate, and other industries. 

So how can you tell these pioneering technologies apart and know what makes them a compatible pair?  

Generative AI: producing creative content 

Let’s start with generative AI. ChatGPT’s ability to spit out uncannily human-sounding new content probably comes to mind.

Generative AI can be defined as artificial intelligence focused on creating models with the ability to produce original content, such as images, music, or text. By ingesting vast amounts of training data, generative AI models can employ complex machine-learning algorithms in order to understand patterns and formulate output. Their techniques include recurrent neural networks (RNNs) and generative adversarial networks (GANs). In addition, a transformer architecture (denoted by the T in ChatGPT) is a key element of this technology.  

An image-generation model, for instance, might be trained on a dataset of millions of photos and drawings to learn the patterns and characteristics that make up diverse types of visual content. And in the same way, music- and text-generation models are trained on massive collections of music or text data, respectively. 

Key examples of generative AI models include: 

  • DALL-E: This platform developed by OpenAI, trained on a diverse range of images, can generate unique and detailed images based on textual descriptions. Its secret: understanding context and relationships between words. 
  • Midjourney: This generative AI platform focused on creative applications lets people create imaginative artistic images by leveraging deep-learning techniques. You can interactively guide the generative process, providing high-level directions that ultimately yield visually captivating output. 
  • Dream Studio: This generative AI platform (which also offers an open-source free version), enables composer wannabes to create music. It employs machine-learning algorithms to analyze patterns in music data and generates novel compositions based on input and style preferences. This allows musicians to explore new and lateral ideas and enhance their creative processes. 
  • Runway: This platform provides a range of generative AI tools for creative professionals. It can come up with realistic images, manipulate photos, create 3D models, automate filmmaking, and more. Artists incorporating generative AI in their workflows can experiment with fine-tuning a variety of techniques. According to the company, “Artificial intelligence brings automation at every scale, introducing dramatic changes in how we create.”

LLMs: Enhancing contextual understanding and memory 

LLMs are a specialized class of AI model that uses natural language processing (NLP) to understand and generate humanlike text-based content in response. Unlike generative AI models, which have broad applications across various creative fields, LLMs are specifically designed for handling language-related tasks. Their varieties include adaptable foundation models.

These large models achieve contextual understanding and remember things because memory units are incorporated in their architectures. They store and retrieve relevant information and can then produce coherent and contextually accurate responses. 

Examples of LLMs include: 

  • GPT-3 (Generative Pre-trained Transformer 3): Developed by OpenAI, this is one of the most prominent LLMs, producing coherent, contextually appropriate text. It’s already being widely used in applications including chatbots, content generation, and language translation. 
  • GPT-4: This successor to GPT-3 supplies advancements in contextual understanding and memory capabilities. As an evolving model, the goal is to further improve the quality of generated text and push the boundaries of language generation. 
  • PaLM 2 (Pre-trained AutoRegressive Language Model 2): Here’s a non-GPT example of an LLM that’s focused on language understanding and generation, offering enhanced performance in tasks such as language modeling, text completion, and document classification. With this functionality, it does a good job of powering the Google Bard chatbot.

Generative AI plus LLMs: a dynamic duo 

Now that you have an idea of how generative AI and large language model technology works in some real-world areas, here’s something else to think about: when they’re utilized together, they can enhance various applications and unlock some exciting possibilities. These include: 

Content generation 

LLMs and generative AI models can produce original, contextually relevant creative content across domains including images, music, and text. For example, a generative AI model trained on a dataset of paintings can be enhanced by an LLM that “understands” art history and can generate descriptions and analyses of artwork.

This content-generation combo is a boon for ecommerce, among other industries. No matter what your online store sells, the technology can generate compelling marketing images and phrasing that helps your brand better engage shoppers. Whether you post AI-aided content on social media or on your site, it can help you more quickly win over customers and increase your sales. 

Content personalization 

By drawing on both generative AI and LLMs, you can expertly personalize content for individual shoppers. LLMs can make sense of shopper preferences and generate personalized recommendations in response, while generative AI can create customized content based on the preferences, including targeted product recommendations, personalized content, and ads for items that could be of interest.  

Chatbots and virtual assistants 

LLMs can enhance the conversational abilities of bots and assistants by incorporating generative AI techniques. LLMs provide context and memory capabilities, while generative AI enables the production of engaging responses. This results in more natural, humanlike, interactive conversations. Again, this technology refinement can ultimately help improve shopper satisfaction.  

Multimodal content generation 

Large language models can be combined with generative AI models that work with other modalities, such as images or audio. This allows for generation of multimodal content, with the AI system being able to create text descriptions of images or create soundtracks for videos, for instance. By combining language-understanding strengths with content generation, AI systems can create richer, more immersive content that grabs the attention of shoppers and other online prospects.   

Storytelling and narrative generation 

When combined with generative AI, LLMs can be harnessed to create stories and narratives. Human writers can provide prompts and initial story elements, and the AI system can then generate subsequent content, all while maintaining coherence and staying in context. This collaboration opens up online retail possibilities that can streamline the products and services lifecycle and boost ROI. 

Content translation and localization 

LLMs can be utilized alongside generative AI models to improve content translation and localization. A large language model can decipher the nuances of language, while generative AI can create accurate translations and localized versions of the content. This combination enables more-accurate, contextually appropriate translations in real time, enhancing global communication and content accessibility.  

Content summarization

Both large language models and generative AI models can generate concise summaries of long-form content. Their strengths: LLMs can assess the context and key points, while generative AI can develop condensed versions of the text that capture the essence of the original material. This ensures efficient information retrieval and lets people quickly grasp the main ideas laid out in lengthy documents.

No, there won’t be a quiz. But we hope this blog post has helped you grasp the basics of what’s going on behind the scenes of these two budding technologies.

Improve your ROI with search AI

One last way that these two functionalities work well together is in enterprise website search. Incorporating aspects of them both in our API, Algolia successfully enhances people’s search experiences on sites ranging from startups to established giants.

We’re ready to provide your online store with industry-leading search optimization that could very well point your revenue in a breakthrough direction. Are you ready to see how AI can start enhancing your search? Contact us or see a demo.

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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