Key Algorithms: Variational Auto-Encoders and GANs 

What sets Generative AI apart is its reliance on sophisticated algorithms, among which variational auto-encoders (VAEs) and generative adversarial networks (GANs) hold prominence. VAEs facilitate the encoding and decoding of intricate patterns within the data, while GANs introduce a competitive dynamic between a generator and a discriminator, pushing the boundaries of creativity through adversarial training. 

Application in Data Augmentation & Computer Vision 

Generative AI is more than just a content creator; it's a game-changer in machine learning. It enhances models by creating extra training examples, supercharging their effectiveness. And that's not all. It brings a touch of realism to computer vision tasks, like recognizing objects and crafting images. 

 With its fancy algorithms and versatile applications, Generative AI doesn't just boost creativity; it teams up with different areas, breaking barriers in the vast world of artificial intelligence. 

What Actually Are Large Language Models? 

Large Language Models are purpose-built AI models designed specifically for processing and generating text that closely resembles human speech. Their focus extends to understanding and mimicking language structures. LLMs derive their proficiency from extensive training on vast text datasets, employing transformer structures. This methodology allows them to capture intricate linguistic nuances, grammatical intricacies, and contextual patterns embedded in the data. 

LLMs pre-training involves teaching them to anticipate the next word in a given text string. This anticipatory learning equips the model with an understanding of language flow, semantics, and contextual relationships within sentences. 

Explanation of LLMs Process 

The functionality of Large Language Models (LLMs) hinges on a series of intricate processes designed to comprehend and generate human-like text. The pre-training phase involves teaching these models to anticipate the next word in a given text string, fostering an understanding of language flow, semantics, and contextual relationships within sentences. Additionally, the transformer structures employed in LLMs play a crucial role in processing and reproducing sequential data with remarkable accuracy. 

You can imagine LLMs as super-smart friends who learn to talk like humans. First, they practice guessing what word comes next in a sentence, getting really good at understanding how words fit together. It's like learning the cool flow of language. Then, they use special tools called transformer structures to process and repeat words in the right order. It's like a magic trick that helps them speak just like us, making them awesome language wizards. 

Quick Comparison - Generative AI vs. LLMs