Generative AI: What is it and how do you use it?

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Generative Artificial Intelligence (AI) is a branch of AI that focuses on creating new content, such as images, text and sound, that did not exist before. It uses complex algorithms to generate new data similar to the data it has analysed. Generative AI is known for its ability to produce realistic and creative output, often in a way that resembles human creativity.

Neural networks are an essential part of Generative AI. They are mathematical models inspired by how the human brain works.

What Generative AI needs to do to work well

To work well, Generative AI needs to perform several tasks. First, it must have a good understanding of the input data, such as images or text, on which it bases its output. This requires sophisticated algorithms for pattern recognition and understanding of context. Second, it must be able to generate new, relevant output that is coherent and consistent with the input data. This means that the AI model must be trained on large data sets to produce accurate results.

What are neural networks?

Neural networks are an essential part of Generative AI. They are mathematical models inspired by how the human brain works. These networks consist of layers of neurons, which transmit and process information. Neural networks learn from examples in the data and adjust their internal parameters to recognise patterns and perform tasks, such as generating new content.

The best-known Generative AI models

Some of the best-known generative AI models are GPT (Generative Pre-trained Transformer), DALL-E and StyleGAN. These models have shown impressive performance in different areas, such as generating text, images and even manipulating styles and features of images.

How to deploy Generative AI as an organisation

As an organisation, you can use Generative AI in several ways. You can use it to generate personalised content for customers, such as recommendation systems or product recommendations. It can also be used to generate creative content, such as logo design or art creation. In addition, Generative AI can be applied in sectors such as medicine. It can be used to discover new drugs or in the automotive industry for designing autonomous vehicles.

Advantages and disadvantages of working with Generative AI

With the right application, Generative AI can add value to various aspects of your organisation. As not all applications are suitable, we list below some of the pros and cons of using Generative AI:


  1. Efficiency gains: Generative AI can automate repetitive tasks, allowing human workers to focus on more strategic tasks.
  2. Creativity: It offers the ability to generate creative content on a large scale, such as images, text and music, which can be valuable for marketing and product development.
  3. Personalisation: Generative AI can be used to generate personalised content based on user data, increasing customer loyalty and engagement.
  4. Innovation: It stimulates innovation by generating new ideas, designs and solutions that might not otherwise have been conceived.


  1. Quality control: Generative AI can be inconsistent in the quality of generated output, requiring close monitoring and manual control.
  2. Bias and ethical issues: It may contain inherent biases arising from the training data, leading to discrimination and other ethical dilemmas.
  3. Data dependence: Generative AI requires large amounts of data to produce accurate results, which can be problematic if this data is unavailable or of poor quality.
  4. Complexity and cost: Implementation and maintenance of Generative AI systems can be complex and costly, especially for smaller organisations with limited resources.

How TriFact365 uses Generative AI for invoice processing

TriFact365 uses Generative AI for invoice processing by applying advanced algorithms to extract and process information from invoices. By using neural networks and machine learning techniques, TriFact365 can automatically extract key data. This includes invoice numbers, amounts, and supplier information, from scanned or digital invoices. This significantly speeds up the invoice processing process and reduces the risk of human error.

TriFact365 can, using this new technique, perform predictive analytics to identify billing trends and patterns, which can help companies optimise their financial processes and control costs. By making smart use of Generative AI, TriFact365 offers an efficient and advanced invoice processing solution to its customers.

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