This ability to generate new content is what sets GenAI apart from traditional AI, which focuses on tasks such as classification, prediction, optimization, and decision-making based on existing data and pre-defined rules.

While GenAI represents a subset of AI techniques, by focusing on learning and generating new data, GenAI expands the scope of AI beyond traditional problem-solving and decision-making tasks.

There are several essential components that are necessary for a functioning GenAI model.

Here are a few of them:

Training Data
Computing Power
Model Architecture
‍Input Prompt
Generated Output

First, a GenAI model needs to be trained. The training process is an iterative and resource-intensive process.

Training Data:
Used to capture patterns, structures, and dependencies, allowing GenAI models to learn and generate new content. High-quality and representative training data is crucial, with the training data's size, diversity, and quality significantly impacting the performance and creativity of the GenAI system.

Computing Power:
Significant computational resources are required for GenAI applications, often using Graphic Processing Units (GPUs) for faster processing and efficient handling of complex algorithms. However, the required computing power depends on factors such as the model's size, task complexity, and available hardware resources. Therefore, this computing power can be distributed across multiple machines or provided by the Cloud.

Model Architecture:
Defines the structure and design of the GenAI model. Different types of architectures can be used, depending on the specific task and the desired output. The architecture determines how the model learns and generates new content.

The learnable components of the model that are adjusted during the training process to improve the model's performance are called parameters. They determine the model's behavior and output and are represented as numerical values or weights.

Deep Neural Networks (DNNs) are a class of artificial neural networks that consist of multiple (deep) layers of interconnected nodes, known as neurons or units. DNNs have quickly become the dominant approach for GenAI model architectures because they allow for quick capture of complex input data patterns and generate cohesive content.

Let's take a look at a couple types of architectures.

Deep Learning Architectures

Generative Adversarial Networks (GANs): Consist of two neural networks—a generator and a discriminator. The generator generates new samples, such as images or text, while the discriminator distinguishes between real and generated samples. Through an adversarial training process, GANs learn to generate content that is increasingly indistinguishable from real data.

Deep Learning Architectures

Transformer Models:
‍Originally introduced for natural language processing tasks and have been adapted for GenAI. Transformers use self-attention mechanisms to capture dependencies between input elements. They have been successfully applied to tasks such as language generation, image generation, and music generation. For example, transformer models can analyze words in a sentence to detect relationships between words and learn how they relate.

LLMs are typically built using Transformer Architectures, trained on huge amounts of text data with a large number of parameters, often in the order of billions. All this allows LLMs to generate coherent text that resembles the patterns and structures present in the training data.

Some examples include:

The GPT-Series, developed by OpenAI

PaLM, developed by Google

LLaMA, developed by Meta