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Limitations and possibilities of prompts
Prompt engineering, prompt design, prompting... whatever you call it, it's like a magic wand. It's a powerful tool for accurately understanding user intent and delivering tailored results. To get the most out of your language models, designing and developing effective prompts is essential.
But just as all magic has its limits, so does prompt engineering. Language models often operate as opaque mechanisms, like “black boxes,” and have limitations on their length. In addition, these models may not yet be suitable for high-level tasks such as complex data analysis or decision making.
But despite these limitations, cutting-edge models like GPT-4 are creating a wave of innovation. This is not just a technological advancement, but a paradigm shift in how we use technology. Let’s compare this to the changes in photography. When cameras first appeared, it was a field that required highly specialized skills. However, with the advent of digital cameras and smartphones, photography has become an art that anyone can easily enjoy.
But with this easy accessibility, the role of the photographer has become more important. Now that taking pictures has become a daily routine, the demand for more delicate and professional work has increased. The same goes for prompt engineering. Various services have emerged, and handling prompts has become a daily skill, but it also requires a more professional and creative approach.
In the future, prompt-based instructions will become easier, but leveraging them to create new services or provide better experiences remains a significant challenge. Learning the art of prompt engineering will help you take advantage of these opportunities more effectively.
The techniques introduced so far will actually be eternal in their basic framework. Just as we always teach and teach people something, they will always be the same. However, methods that are not techniques will be better. For example, techniques such as ToT, CoT, and ART are interesting, but many parts have been simplified with the emergence of Embedding models in language models.
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