So far, we have discussed five quintessential categories of generative AI’s use cases, which are generation, translation, summarisation, rewriting and classification. In this segment, we will continue that discussion and talk about the next two categories: extraction and analysis.
Let’s start by understanding the use cases where generative AI models can be used for searching or extracting tasks.
As Kshitij mentioned, large language models are exceptionally good at going through a large collection of documents and finding relevant pieces of text, snippets of code or numbers. An example Kshitij provided in this category was using all the information in a customer’s journey at a company and extracting information that can be used to enhance customer support calls. This kind of functionality can be replicated on general databases and complex legal, financial or insurance-related documents.
In the next video, Kshitij will discuss the next category, which is analysis or interpretation.
As discussed by Kshitij in the previous video, the analysis use case of generative AI models can also be used to reason about or interpret large language models can be used to identify and solve common issues that are received in the form of customer feedback. This functionality extends to other domains such as software engineering, data science and finance.
Before moving forward, answer the following question to check your understanding.
You have now explored all the different categories of generative AI’s application. You can now be on the lookout for the tasks that belong to these categories being performed in project/workplace. You might just find a new use case of generative AI in your own context.
In the next segment, you will learn about the skills required to harness the capabilities of generative AI.