The reason why prompt engineering is necessary is because generative AI such as ChatGPT is being introduced in various fields. In a situation where various generative models are emerging, learning the characteristics and specialized strategies of each model and being able to quickly create prompts that can consistently and consistently deliver outputs that are suitable for customer requirements is one of the important factors in verifying and realizing service ideas.

Good prompt engineering can also optimize the services that a company operates. It starts with reducing the number of unnecessary API calls, and it can even reduce the number of tokens for prompts that bring the same output through token optimization of prompts. In situations where output is important, even a basic model can produce stability and output comparable to a flagship model, and through various prompt strategies and service implementations, specific functions of the service can enable high-level inference. This is also directly related to the satisfaction and operational efficiency of services with AI-based functions.

Of course, due to the nature of generative artificial intelligence, no matter how good a command you create, it is not easy to obtain the desired or ideal result at once. Therefore, prompt engineering exists to reduce time and cost, but we must be wary of the leap forward thinking of a deus ex machina that solves all solutions through generative artificial intelligence.

Source: namuwiki, https://en.namu.wiki/w/%ED%94%84%EB%A1%AC%ED%94%84%ED%8A%B8%20%EC%97%94%EC%A7%80%EB%8B%88%EC%96%B4%EB%A7%81
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