Data Collection

The first step in Generative AI is to collect and preprocess a large amount of data. This data serves as the training set for the AI model. The type of data collected depends on the desired output. For instance, if the goal is to generate human-like text, the training data might consist of large text corpora. Preprocessing involves cleaning the data, removing irrelevant information, and transforming the data into a format that can be fed into a machine learning model.

Model Training

The second step involves training a generative model on the preprocessed data. There are several types of generative models, including but not limited to, Generative Adversarial Networks (GANs), Variational Auto-encoders (VAEs), and Long Short-Term Memory Networks (LSTMs).

These models learn the underlying patterns and distributions of the training data. For instance, in a GAN, two neural networks – a generator and a discriminator – are trained simultaneously. The generator tries to create fake data to fool the discriminator, while the discriminator tries to distinguish between real and fake data. Through this adversarial process, the generator learns to produce data that closely mimics the real data.

Data Generation

Once the model is adequately trained, it can generate new data. This is done by feeding the model a random seed or input, and the model outputs data that mimic the patterns and structures it learned during training. For example, a GAN trained on images of faces can generate new images of faces that do not exist but look convincingly real. Similarly, a LSTM trained on text can generate human-like sentences or even entire articles.

Step 1

Land

The setup of the database is a critical aspect of this phase, followed by the selection of a Language Model (LLM). Subsequently, the solution architecture will be designed to accommodate the specific use case and a robust LLM governance framework will be established. At this stage, ethics are paramount; considerations around the ethical use of AI and data are factored in to ensure compliance with regulations and company standards. A deployment framework is created, paving the way for a smooth transition to practical application.

Step 2

Stand

The “Stand” phase commences with the deployment of the LLM. A suitable infrastructure is established, designed to support the efficient operation and scalability of the AI model. Simultaneously, the data flow setup ensures that the necessary data inputs and outputs are correctly channelled. Modularity is considered during the infrastructure design to support the smooth integration of the solution into existing systems. Security protocols are reinforced to protect data integrity and privacy, while the system is continuously monitored and maintained to ensure optimal performance.

Step 3

Expand

The final phase, “Expand”, focuses on refining the AI model based on user feedback and additional requirements. During this phase, the solution is optimised further to improve performance and deliver enhanced results. Techniques such as the use of adapters may be employed to tune the model, improving its adaptability and effectiveness. The “Expand” phase ensures the solution not only meets the initially defined use case but also evolves over time, adapting to changing business needs and continuously enhancing its value proposition.