Introduction: In Lesson 1, we learned about the role of prompts in establishing context and providing guidance to AI models. In this lesson, we will delve deeper into the art of providing clear instructions within prompts. Clear instructions are essential for guiding AI models and ensuring they generate accurate, relevant, and desired outputs. Let’s explore some strategies for crafting precise and unambiguous instructions.
📚 Strategy 1: Specify the Desired Output Format When crafting prompts, it is crucial to specify the desired format or structure of the output. By clearly defining the expected output format, you guide the AI model to generate responses that adhere to your requirements. This ensures that the model understands the intended format and can tailor its response accordingly.
[Example 1] Code Generation Prompt:
📝 Prompt: “Write a Python function named ‘calculate_average’ that takes a list of numbers as input and returns their average.”
Explanation: In this example, the prompt provides specific instructions for the desired output—a Python function named ‘calculate_average’ that takes a list of numbers as input and returns their average. By specifying the function name, input format, and expected output, the prompt guides the AI model to generate code that precisely meets these requirements.
[Example 2] Text Generation Prompt:
📝 Prompt: “Write a persuasive paragraph of approximately 150 words advocating for renewable energy sources.”
Explanation: Here, the prompt specifies the desired output format—a persuasive paragraph advocating for renewable energy sources with a target word count of approximately 150 words. This clear instruction guides the AI model to generate a focused and persuasive response within the given constraints.
📚 Strategy 2: Define Constraints and Criteria In addition to specifying the desired output format, it is often beneficial to define constraints or criteria for the AI model to follow. This helps in narrowing down the scope and aligning the responses with specific requirements or limitations.
[Example 1] Image Captioning Prompt:
📝 Prompt: “Provide a concise caption for the given image that highlights the emotion of joy.”
Explanation: In this example, the prompt not only instructs the AI model to generate a caption for the image but also specifies the emotion to focus on—joy. This constraint guides the model to generate captions that capture the essence of joy in the given image.
[Example 2] Language Translation Prompt:
📝 Prompt: “Translate the following English paragraph into Spanish, maintaining the original tone and style.”
Explanation: Here, the prompt instructs the AI model to translate the paragraph while emphasizing the need to preserve the original tone and style. This constraint ensures that the translated output maintains the intended nuances and characteristics of the source text.
📚 Strategy 3: Break Down Complex Tasks If the desired task is complex, it can be beneficial to break it down into smaller, more manageable subtasks. By dividing the task into clear steps or instructions, you provide the AI model with a structured approach, enabling it to generate responses more effectively.
[Example 1] Story Writing Prompt:
📝 Prompt: “Write a science fiction story with the following elements: time travel, an alien civilization, and a moral dilemma faced by the protagonist.”
Explanation: In this prompt, the complex task of writing a science fiction story is broken down into three distinct elements—time travel, an alien civilization, and a moral dilemma. By providing these clear instructions, the AI model can focus on incorporating each element into the story, resulting in a well-structured narrative.
[Example 2] Research Summary Prompt:
📝 Prompt: “Read the provided research paper and summarize its main findings in three key points.”
Explanation: Here, the prompt divides the task of summarizing a research paper into three key points. By breaking down the task, the AI model can concentrate on extracting and summarizing the most significant findings, ensuring a concise and informative summary.
By following these strategies and providing clear instructions within prompts, you empower AI models to generate responses that align with your specific requirements. Clear instructions not only enhance the accuracy and relevance of AI-generated outputs but also streamline the prompt-engineering process.