Generative AI (GenAI) and Large Language Models (LLMs) have enabled us to achieve feats comparable to those seen in science fiction films, such as writing a Shakespearean poem set in a Star Wars universe.
These two technologies are behind some of the most popular tools in the world, such as ChatGPT, which has around 3.7 billion monthly visits.
While GenAI and LLM are closely related, they're not quite the same thing.
Let's dive into what they are and how they work and overlap with some practical examples!
What Is Generative AI (GenAI)?
Generative AI is a subset of Artificial Intelligence (AI) that focuses on creating content, such as text, images, audio, video, or code.
This AI subset uses Deep Learning (DL), a type of Machine Learning that mimics the structure of the human brain using artificial Neural Networks (NN) to learn from data, making it capable of working on a wide range of tasks.
Apart from multiple format content creation, GenAI tools can be used to summarize text, solve math problems, find errors in code, analyze data content translation, create graphics, automate tasks and generate voice.
How Does GenAI Work?
GenAI models undergo extensive training, where Artificial Neural Networks process large datasets until they're able to recognize patterns.
After that, the models learn to make decisions and predictions driven by probability.
So, you can think of Generative models as probabilistic engines that produce outputs based on their understanding of patterns in training data.
In this manner, they can recognize and create the shapes and colors of objects such as cities, people, grains of coffee and practically anything.
Generative AI models can be trained using two techniques: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
First, GANs use two Neural Networks, a generator that creates content and a discriminator that validates the content by comparing it with real content.
For example, the generator creates a realistic image of a flower based on its training data and the discriminator checks if it does look like a flower by comparing it to a real one.
VAEs, on the other hand, aim to identify the most important or "latent" variables or aspects of an object so they can produce new outputs that look like the original input.
So, thanks to VAEs, GenAI can learn the core aspects of an object, allowing it to create realistic variations.
GenAI models also use an advanced Machine Learning technique called Reinforcement Learning (RL), which allows them to enhance their capabilities through trial and error.
What Are Large Language Models (LLMs)?
LLMs are a GenAI subset that focuses on understanding human language by applying Natural Language Processing (NLP).
With a deep understanding of grammar and semantics, LLMs can process text and write coherent and accurate responses to user input based on context.
On top of that, LLMs can translate text and support multiple languages while also allowing users to get answers in a particular style and tone for more relevant responses and tailored communication.
Unlike traditional chatbots, LLM-powered chatbots can use conversational memory, allowing them to remember past conversations with users.
This feature, combined with their impressive ability to mimic human-like conversations, makes LLMs excellent tools for seamless customer service and Natural Language Processing tasks.
How Do LLMs Work?
LLMs have the word "large" in their name because they are trained with billions of parameters, which are adjustable variables within the model that help it identify patterns, relationships and meanings in the data.
The Transformer Architecture also plays a huge role in how LLMs work; in fact, the Transformer represents the "T" in OpenAI's model GPT, which stands for Generative Pre-trained Transformer.
LLMs can process individual words from user inputs in the form of numerical data called embeddings by using the Transformer Architecture.
Processing embeddings allows LLMs to understand the semantic meaning of words and make predictions based on a contextual understanding.
For example, if you give them part of the popular idiom "Costs and arm and a ...", they will predict "leg" is the missing word because they've seen that pattern multiple times.
GenAI vs LLM
A key distinction between GenAI and LLM is the way they're trained. GenAI relies on GANs, while LLMs in the Transformer Architecture.
Although both have broad capabilities for content generation, GenAI focuses on generating a wide range of creative content (images, video content, text and music) and LLMs on language-related tasks.
That's why LLMs are considered a subset of GenAI, which only aims to understand and generate human-like text.
In this manner, GenAI and LLMs have different use cases and applications. However, they can complement each other to streamline processes in vast amounts of fields.
For example, in User Experience (UX) Design, LLMs can help analyze and organize data from customer feedback to create detailed User Personas.
Then, a GenAI art model like Midjourney can help designers create content like images and videos that align with them.
Similarly, in data analysis and visualization, LLMs can assist in data processing tasks such as data cleaning and organizing.
After that, GenAI can quickly illustrate that data with graphics and charts.
Conclusion
GenAI and LLMs are significant AI breakthroughs and while they're closely related, they have different real-world applications.
LLMs specialize in understanding and generating human-like text and GenAI involves a wider range of original content, including images, audio and video.
As a UX-driven Full-cycle Product Development agency with over 14 years of experience, we fully understand the importance of the power of LLMs and GenAI streamlining processes and operations.
Reach out if you'd like to hear about our unique UX approach to bringing even the most disruptive digital products to life.
