Understanding large language models vs. generative AI

You’ve most likely heard of both large language models (LLM) and generative AI (Gen AI), especially after the boom that was ChatGPT. But what exactly are they? And why do people tend to use these terms interchangeably?  

Well, they are two different things but it’s a bit complicated. Let’s take this analogy for example: 

GenAI solutions are often trained on LLMs and their vast data sets to produce new and unique content. Just like how Gordan Ramsey was trained on hundreds of recipes and techniques from around the world, and that’s what inspires him to create new and unique dishes. See the connection? 

In essence, while GenAI creates entirely new content based on its understanding of data, LLMs retrieve existing content from their data set based on your input. Both approaches serve the purpose of providing content based on user input, but they operate in fundamentally different ways. 

Let’s dive a bit deeper into both technologies to better understand them. 

LLMs are a type of generative AI. They use natural language processing (NLP) to understand and generate human-like language outputs. LLMs are trained on massive amounts of language data from various sources.  

LLMs have something called a transformer model, which is often compared to the neural network of the brain. This transformer can encode and decode data so that the LLM can analyze and understand text by paying attention to how words and phrases relate to each other in a sequence. 

LLMs are strictly used for language and text related tasks. Here are a few real-life examples of LLMs:

Generative Pre-trained Transformer, better known as GPT, was launched by OpenAI last year, and is probably one of the most widely known LLMs in the world right now. GPT is used for text content generation, powering chatbots, translations, summarizations, and so on.

LaMDA is an LLM by Google, that is specially trained on text data, meaning it can only understand and generate text outputs (like most LLMs). LaMDA is used in unison with other LLMs by Google to power their Gemini AI solution.  

Llama is another example of an LLM. It’s a language model that is also capable of generating code and language about code, which makes it a great tool for developers to help aid in speeding up programming.

Recently LLMs have evolved to understanding content other than just text. These are known as multimodal LLMs. These LLMs can decipher different modalities of content like images, videos, and audio to make conversing with users more dynamic.  

Let’s revisit our analogy of the LLM recipe library. A multimodal LLM would be able to process an image of a dish, say shepherd’s pie, recognize it and provide a recipe that could be used to replicate it. 

GenAI focuses on creating new content. Traditional AI systems are usually designed for specific tasks or problem-solving, but GenAI is able to generate new content – anything from images, text, music, video, and more. Unlike LLMs that are built on language-based data.  

Generative AI works by training algorithms on large datasets, enabling them to learn patterns and structures inherent in the data. Once trained, these algorithms can generate new content that mimics the style, characteristics, and distribution of the training data. 

Unlike LLMs, generative AI can understand, analyze, and produce different types of content depending on the machine learning algorithm it has been trained on. Here’s a short list of some of the more popular types of GenAI:

This generative model learns and uncovers patterns and irregularities in the input data so that it can create new and unique outputs that could have been part of the original input. For example, if the model was trained on human faces, it would understand the structure and patterns in a facial structure to generate the image of a person that doesn’t exist. This model is mostly used in text-to-image generative AI used in unison with other models to give us DALL-E or Midjourney. 

This is an advanced machine learning algorithm that adds noise to input data so that it can learn to reverse and remove the noise and get the data back to its original form. This type of generative AI has become extremely successful in producing new images from text descriptions, completing images, and manipulating images.

Transformer models opened the door for LLMs by advancing natural language processing with generative AI. They work by paying attention to how different words or parts of a sentence relate to each other. One thing about transformers is they can look at many words at the same time and understand them together. This makes transform models good at making sense of long pieces of text and coming up with new text based on what it’s learned. 

NeRFs can generate 3D content from multiple 2D images. Essentially, it can analyze images of the same item from different angles to produce an accurate 3D model of the image. This GenAI model can be useful in a number of fields such as architecture and robotics.

VAEs are a kind of GenAI model that can create new content. It uses an encoder to compress data into a representative form, and then a decoder to return it back into its original form. The encoder helps the algorithm understand the input data, while the decoder helps it generate something new. This model can be used for images, music, text, etc. 

OpenAI’s DALL-E model is their text-to-image GenAI. Essentially users can input a sentence or description and DALL-E generates an image reflective of the input. You can go back and forth with the AI until it has produced a visual that the user is satisfied with. It cannot produce anything other than a visual output, although it understands the language of written text from the user.

Another GenAI from OpenAI, Sora is a text-to-video GenAI that takes a simple description and produces a video output. Still in its BETA phase, Sora is still working out some kinks and is only available to some artists and professionals for testing.

A text-to-audio GenAI tool from Meta allows users to unput a text description and get a high-quality output of music or audio. It consists of three AI models that allow it to produce music, sound effects and other audio.   

So, now that you understand the difference between LLMs and GenAI, we can start to get a grasp on how and when they can work together to create powerful tools in the real world.

LLMs like GPT can understand and generate human-like text. GenAI algorithms can be used to create chatbots with personality and creativity. The LLM provides the framework for understanding and responding to customer queries, while GenAI adds flair and individuality to the conversation, making the interaction more engaging and natural.  

GenAI and LLMs can collaborate to create personalized content for customers. For example, an LLM might generate a personalized message to a customer, while GenAI, such as a text-to-image generator, brings the description to life by creating an image based on the text. This collaboration can result in unique and imaginative creations that blend the power of language and visual art. 

Large language data sets can power tools like chatbots, but it might become limited if the data is only in one language. GenAI translation models can help broaden the scope by translating the outputs of LLMs to provide support or outputs in different languages. 

Arguably the biggest challenge with LLMs and GenAI are false outputs, or AI hallucinations. LLMs are trained on such large amounts of data and can produce texts that aren’t logical. When LLMs are not trained properly, they can be a danger to users, if for example it is being used to share legal or medical advice.  

Learn how Infobip achieved 0 hallucinations with its GenAI solution:

LLMs and GenAI are only as intelligent and knowledgeable as we train them to be. So, if these algorithms are trained on biased data, they will in turn produce biased responses. This leads to some ethical concerns around the use and training of these solutions.  

Read more about how to ensure your AI solution is ethically sound:

LLMs need vast amounts of data to be trained properly – high quality text data at that. This leads to a problem with data collection, how to collect appropriate data, and using customers’ personal data to train these models. This poses a risk with how AI uses customer data to produce outputs. It is key to ensure the solution provider follows all compliancy and regulation laws around data and AI training as to avoid any unethical uses of personal data.

Today, whatever the industry, GenAI and LLMs have a large influence of the innovations coming from businesses and solution providers. Implementing a solution with GenAI and LLMs can help businesses resolve some major pain points:  

We are bound to see more advancements come from GenAI and LLMs in the future. Understanding the nuances between GenAI and LLMs is important in harnessing the full potential both technologies carry. Brands can benefit greatly from implementing LLMs and GenAI solutions with the right solution provider, tailored to their needs and use cases.