In simplest possible terms, generative AI uses algorithms to process data and generate new output. In other words, it generates new data that is similar to the original training data. It gets this capacity through a process of learning patterns in the input data and uses that learning to generate new data that syncs with those patterns.

Generative AI thus uses pre-trained, large language model that provides the users output in the form of text, images and other content. This is done in response to text-based prompt.

Such chatbots could be of immense help in customer service. These can answers queries. They can summarise and condense policies. They can prepare promotional material and product manuals.

Salesforce, a California based company, has announced Einstein GPT to create personalised content, emails and targeted messages.

Generative AI can auto-generate code for the programmers.

AI is predicted to generate 10 per cent of all data and 20 per cent of test data.

Generative AI can be used in banking, finance and insurance. It can be used in new drug development and fashion design. It can assist conduct of meetings — prepare summaries, transcriptions and content. It can be used in conjunction with syntax algorithms.

On integration with organisation’s systems, generative AI can take commands such as ‘prepare the report’, ‘refine this offer’ or ‘create an application.’

The data fed as input to this model must be decent and unbiased. It needs protection.

Generative AI facilitates automation, augmentation and acceleration.

Of course, in precision tasks where being error-free is important, the model is not suited. There are legal issues too while it uses copyright material.