Identifying Generative AI Use Cases
In the previous chapter, we discussed how the use case exploration and proof of concept development process has shifted with the emergence of large language models (LLMs). Specifically, the ability for rapid experimentation with LLMs has led teams to favor an experimental approach over more traditional requirements analysis and design processes.
With LLMs, use cases can quickly be tested by creating prompts that demonstrate potential capabilities. This allows for greater flexibility and speed than meticulously mapping detailed requirements upfront. Of course, once promising use cases are identified, more rigorous analysis is highly recommended. Additionally, security, monitoring, and governance of production systems remain critical components.
In this chapter, we will discuss an approach to identify promising use cases to explore with LLMs. We categorize use cases based on how an application interacts with the LLM. This provides a framework...
