Prompt engineering plays an important role in understanding the capabilities and limitations of large language models (LLMs), says Olivia Tanuwidjaja. “The prompt itself acts as an input to the model, which signifies the impact on the model output. A good prompt will get the model to produce desirable output, whereas working iteratively from a bad prompt will help us understand the limitations of the model and how to work with it.”
This article explains the principles and practices of building prompts and describes how data analysts “can leverage their context knowledge, problem-solving skills, and statistical/technical capabilities” in this new area.
Read more at TowardsDataScience.
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