So far, you have seen several use cases of generative AI in different industries and functions. The applications and use cases may be in many different contexts, but once we abstract away the details, we find that there are a few well-defined tasks that generative AI models can perform.
For instance, in the example of the MakeMyTrip chatbot that you saw earlier, the generative AI model interprets the user’s input to search for and curate the right travel plan. Similarly, in the case of BloombergGPT, the generative AI model searches for the right information and extracts data.
We have condensed the different tasks performed by generative AI models into seven quintessential categories. They are as follows:
- Generation
- Translation
- Summarisation
- Editing or rewriting
- Classification or tagging
- Search or extraction
- Analysis or interpretation
Through the next few segments, we will examine these quintessential categories. They are quintessential because, on peeling back an application’s layers of complexity, you will find that generative AI is being used to perform a task that lies in one of these categories. This discussion will also provide you with a mental framework to think about situations where generative AI might be useful.
In the next video, Kshitij will talk about the first category, which is generation.
Kshitij gave you a few examples of generative AI’s use cases involving generation such as generating product descriptions, advertisements, training modules and so on. You also saw how text image, code, and video generation find relevant use-cases across a variety of functions and domains.
In the next video, Kshitij will talk about the next two categories: translation and summarisation.
Kshitij discussed a fed examples of generative AI’s use cases involving translation such as translating user queries for users who speak different languages, translating marketing content to target people from a different demographic and translating policy documents within a company.
The summarisation-based use cases of generative AI models allow users to skim through large amounts of information in less time. For example, generative AI could be used to summarise meeting notes, legal documents, emails and so on. Kshitij provided an example of summarisation being used in the case of customer feedback. As you saw in this example, this may allow a product designer to spot the trends in the feedback they get from customers and turn them into actionable insights.
Before moving forward, answer the following question to check your understanding.
So far, you have learned about three categories of generative AI’s use cases: generation, translation and summarisation. In the next segment, we will continue our discussion on the quintessential categories of generative AI’s use cases.