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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.”
Prompt engineering is a growing field, and it requires specific techniques that may not be part of a typical data analytics skillset, Tanuwidjaja says.
This article shares the principles and techniques of building prompts and looks at how data analysts “can leverage their context knowledge, problem-solving skills, and statistical/technical capabilities” in this new area.
Read more at TowardsDataScience.
See also:
Beginner’s Guide to Data Science
Get Started with Data Analytics in Python
What’s a Data Scientist?
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