Prompt Engineering for Optimal AI Responses
Just as people need clear instructions to work properly, generative AI also requires proper guidance.
However, AI can sometimes provide irrelevant answers or overwhelm users with overly detailed responses to simple questions.
Prompt engineering refers to the process of optimizing prompts to enable AI to generate desired responses.
Vague and ambiguous prompts can lead to AI providing unsatisfactory results, causing unnecessary time wastage.
In contrast, well-crafted prompts help AI deliver more accurate and useful answers.
In this lesson, we will explore the concept of prompt engineering and how to improve prompts through various examples.
Examples of Prompt Engineering
Let's look at how different prompts can yield different results in the same situation.
Unstructured Prompt
I'm planning a workout schedule at the gym.
From June 1 to 10, 2024, every other day,
tell me what exercises I should do each day along with a meal plan.
The above prompt requests a workout schedule and meal plan, but the AI's response may be cluttered with unnecessary information, making it difficult for the user to quickly identify the desired information.
What if we refine the prompt as follows?
Well-Organized Prompt for Clear Responses
I'm planning a workout schedule at the gym.
From June 1 to 10, 2024, every other day,
tell me which exercises to do each day
along with breakfast and dinner meal plans in table format.
---Table Format---
### Title
| No. | Date | Day | Exercise | Breakfast | Dinner |
You can see that the workout schedule, along with the meal plans, is neatly organized in a table format.
Prompt engineering not only involves specifying the format of responses to enhance readability but also helps prevent incorrect answers from the AI.
In the next lesson, we will delve into the delimiters (e.g., ###, ---) used in improved prompts.
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