So what is the real difference?

Despite the fact that these two sound totally divergent, there remains the fact that their respective use cases have plenty of similarities. To be honest they’re not mutually exclusive and are more often used to complement each other as copilots, and when they do, they can be very powerful. Probably responsible for AI’s current rise in popularity.

You may not be aware, but AI has been around for almost seven (7) decades! And it has not really progressed beyond ‘pattern matching’. It is definitely not the sci-fi promoted sentient technology that will remove humans from the planet. So where is the magic, and what makes them ideal partners?

Generative AI

Let’s start with generative AI and ChatGPT’s incredible ability to spit out human-sounding new content.

Generative AI is best defined as artificial intelligence that creates models that produce original content, such as images, music, or text. They work by ingesting vast amounts of training data and use complex machine-learning algorithms in order to find and understand patterns and then formulate output.

An image-generation model, for example, is trained on a dataset of millions of photos and drawings to learn the patterns and characteristics that make up the 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 to find and understand patterns and then formulate output.

Basically, they sift through a whole bunch of known knowns to be able then to predict the likely output from provided inputs. Not overly smart, but just needs loads of training data and lots of processing power!

Large Language Models (LLMs)

These models achieve contextual understanding and remember things because memory units are built into their architectures. They store and retrieve relevant information and can then produce coherent and contextually accurate responses. This is the 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.

Merging Generative AI and LLMs

When they’re utilized together, they can enhance various applications and unlock some really cool possibilities. Let’s check out a few now. 

Content generation 

Together they can produce original, contextually relevant creative content across multiple such as 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.

Content personalization 

Together you can expertly personalize content for individual users. LLMs can make sense of user preferences and generate personalized recommendations in response, while generative AI can create customized content based on the preferences, including targeted recommendations, personalized content, and prompts that could be of interest.  I have been teaching to ‘Recommendation Engines’ since 2017!

Chatbots and virtual assistants 

For businesses, together they can enhance the 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.

Multimodal content generation 

Together they can be combined to 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. The combination can create richer, more immersive content that grabs the attention of users.