Sometimes the easiest way to explain what you want is to show it. Few-shot prompting puts a handful of examples inside your prompt so the model can pick up the pattern, whether that is a tone of voice, a label to assign, or a format to follow.
You'll start with the difference between zero-shot, one-shot, and few-shot prompting, then see how examples in a prompt guide a language model toward the answer you want. From there you'll watch the technique put to work in ChatGPT. As you practice, notice how the quality, variety, and consistent format of your examples affect the results.
Examples help a model copy a pattern, but harder problems need it to think things through. That's the focus of Reasoning Techniques, the next module in this course.
An introduction to LLM prompting that compares zero-shot, one-shot, and few-shot approaches.
How including examples in your prompt helps a language model respond better and gives you the output you want.
A look at the technique of asking a model to do a task by giving it a few examples first.
How to use few-shot prompting in ChatGPT by providing a few examples inside your prompt to guide the AI.