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.
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.
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.
The most common and accessible use case currently. This involves the creation of completely new assets by the AI. It is often used in creative applications such as creating new designs, text, or music.
- Code – AI-driven code generation can also result in more standardized and maintainable code, as the AI can be programmed to follow best practices consistently. This benefit is particularly useful in large software projects where maintaining code quality and consistency across the team can be challenging.
- Marketing Assets – Generative AI can automate the creation of marketing materials such as advertisements, social media posts, email campaigns, and more. By learning from previous successful campaigns and current market trends, the AI can generate compelling and effective marketing assets.
- Datasets – Sometimes, the necessary data for a specific task may be lacking, too expensive, or impossible to collect due to privacy or ethical concerns. In such cases, Generative AI can create synthetic data that closely mimics real-world data, enabling continued research and development.
Synthesis involves using existing data and extending it in new and creative ways. This could involve combining different datasets to create a more comprehensive view, or it could involve transforming existing data into a new format.
- Meeting Notes – Streamline the process of taking and summarising meeting notes, turning unstructured conversation into clear, concise, and actionable summaries.
- Sentiment Analysis – Sift through large volumes of text, such as customer reviews or social media comments, and categorise them based on sentiment (positive, negative, or neutral).
- Video Editing – With Generative AI, video editing becomes more accessible and efficient. AI can automate various aspects of video editing, from basic tasks like trimming and cropping to more complex tasks such as colour correction, audio enhancement, and even content creation.
Analysis is about making unstructured data more accessible for further processing and analysis. This often involves turning data that is not easily quantifiable, such as text, images, or video, into structured data that can be analysed using traditional data analysis techniques.
- Complaints Detection – Automatically detect and categorise customer complaints in large volumes of unstructured data, such as emails, social media posts, or customer service transcripts.
- Clustering – Clustering is a form of unsupervised machine learning where the AI groups similar items together. In the context of Generative AI, this could be used to analyse a broad range of unstructured data – from customer feedback to sales data – and identify patterns or trends that might not be apparent through manual analysis.
- No-Code APIs – Enable the creation of No-Code APIs (Application Programming Interfaces) that allow non-technical users to interact with complex data sets and perform sophisticated analysis tasks without having to write any code.
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.
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.
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.