Chapter 1. Generative AI Use Cases, Fundamentals, and Project Life Cycle
In this chapter, you will see some generative AI tasks and use cases in action, gain an understanding of generative foundation models, and explore a typical generative AI project life cycle. The use cases and tasks you’ll see in this chapter include intelligent search, automated customer-support chatbot, dialog summarization, not-safe-for-work (NSFW) content moderation, personalized product videos, source code generation, and others.
You will also learn a few of the generative AI service and hardware options from Amazon Web Services (AWS) including Amazon Bedrock, Amazon SageMaker, Amazon CodeWhisperer, AWS Trainium, and AWS Inferentia. These service and hardware options provide great flexibility when building your end-to-end, context-aware, multimodal reasoning applications with generative AI on AWS.
Let’s explore some common use cases and tasks for generative AI.
Use Cases and Tasks
Similar to deep learning, generative AI is a general-purpose technology used for multiple purposes across many industries and customer segments. There are many types of multimodal generative AI tasks. We’ve included a list of the most common generative tasks and associated example use cases:
- Text summarization
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Produce a shorter version of a piece of text while retaining the main ideas. Examples include summarizing a news article, legal document, or financial report into a smaller number of words or paragraphs for faster consumption. ...
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