About Demand Engineering Technique to Maximize ChatGPT Results
Have you ever used ChatGPT to help complete a specific task, but the results were not what you expected? Perhaps this happened because your request was not good enough or detailed.
In this article, we will discuss the demand engineering technique to help you maximize the results of ChatGPT. One of the techniques we will discuss is few-shot standard prompts.
Few-shot standard prompts are standard requests that have been enhanced with task examples within them. These task examples help ChatGPT understand the task you want to complete and increase your chances of getting the desired results. Few-shot standard prompts consist of task descriptions, examples, and requests.
One advantage of few-shot standard prompts is that you can better control ChatGPT's behavior. By providing examples, you provide clues about the type of output you want. In this case, label space (all possible labels for the given task) is more important than the actual answer in the example.
Here is an example of using few-shot standard prompts to extract airport codes from text:
Standard Request: Extract airport codes from this text: “I want to fly from Orlando to Boston”
Few-shot standard prompts: Extract airport codes from this text: Text: “I want to fly from Los Angeles to Miami.” Airport codes: LAX, MIA
Text: “I want to fly from Nashville to Kansas City.” Airport codes: BNA, MCI
Text: “I want to fly from Orlando to Boston” Airport codes:
In this example, ChatGPT is asked to extract airport codes from three different sentences. This way, ChatGPT can better understand the requested task.
When using few-shot standard prompts, it is important to include enough and varied examples. You can also use random labels from the label space to improve your request results.
By using demand engineering techniques like few-shot standard prompts, you can maximize ChatGPT results and increase effectiveness in completing tasks. Don't hesitate to try this technique and see the difference in results!
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