White paper: The Prompt Engineer’s Playbook

We’ve all engaged with ChatGPT at some point, yet its potential might surpass your expectations. The possibilities are enormous, limited only by your imagination. This white paper aims to provide insights and tips to enhance the efficiency of your experience with Large Language Models.

What is prompt engineering?

Prompt engineering can be described as the technique that involves crafting inputs, such as text or images, for generative AI models to define and limit the range of responses the model can generate. This technique usually aims to elicit specific outcomes without adjusting the model’s actual weights, distinct from fine-tuning.

In more universal terms, it is a way to guide the model’s output without altering its underlying structure or validity. In essence, you are giving instructions or directives to the Language Model in order to receive an appropriate and beneficial answer. Moreover, it is often referred to as “in-context learning,” because examples are used to provide further direction to the model. Optimally, this is done in the structure of a conversation with the Artificial Intelligence model. This way, additional instructions can be given based on a former output.

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