11 Best Practices for Prompt Engineering That Are A Game-Changer

Prompt engineering is a crucial skill for anyone working with language models, whether it's for chatbots, content generation, or task completion. By crafting well-designed prompts, you can elicit more accurate, relevant, and engaging responses from AI systems. In this blog post, we'll explore the best practices for prompt engineering and provide you with a comprehensive guide to help you master this essential skill.

What is a prompt?

At its core, a prompt is the input you provide to a generative AI model to perform a specific task. When you interact with models like GPT-3 or ChatGPT, you do so primarily through textual inputs. These inputs, or prompts, guide the model to generate the desired output.

For instance, in image generation models like DALLE-2 or Stable Diffusion, the prompt is typically a detailed description of the image you want to create. With Bestever, an AI-powered ad creation tool, the prompt can be a detailed concept of your display ad. 

The breadth of possibilities is vast, and this is where prompt engineering becomes invaluable.

Elements of a prompt

Prompts can vary in complexity, but generally, they include a few key elements:

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  Context: Sample outputs that help guide the model's responses.

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  Instruction: Clear directives that tell the model what to do.

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  Input data: Relevant information or context that the model can use to generate a more accurate response.

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  Output indicator: Specific queries you want the model to answer.

Understanding these elements is fundamental when exploring prompt engineering examples and applying prompt engineering tips effectively.

What is prompt engineering?

Prompt engineering is the art of creating prompts that effectively communicate your intent to an AI system. It involves carefully selecting words, phrases, and formatting to guide the model towards generating the desired output. A well-crafted prompt can make the difference between a mediocre response and a brilliant one.

Best practices for prompt engineering

When crafting prompts for AI, it's crucial to ensure they elicit the most accurate, relevant, and useful responses. Here are 11 best practices for prompt engineering:

1. Be clear 

The first and most important principle of prompt engineering is clarity. Your prompt should be clear and concise, leaving no room for ambiguity. Use precise language and avoid vague or open-ended questions.

In the prompt above for generating a product photo using Bestever, clarity is achieved by specifying the visual setup (contrasting gradient background), targeting the audience with "classic elegance for luxury women," and including technical details like "4k, uhd, sharp, high detail." This ensures precise and high-quality photo creation aligned with luxury branding. Start using Bestever today to create product photos on the go without hiring an expensive photographer. 

2. Set context

Provide relevant context and background information in your prompt to help the AI system better understand the task at hand. This can include details about the target audience, tone, style, or any specific requirements, such as:

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  Background Information: Introduce the AI to the topic or scenario you’re focusing on.

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  Specific requirements: Define what exactly you want from the AI, whether it’s a technical explanation, a creative story, or a structured list.

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  Constraints: Specify any boundaries or limitations, such as word count, style, or tone.

Example: If you’re asking the AI to explain deep learning, provide some context: "Assuming I am a beginner in the field of AI, explain the concept of deep learning and its applications." This helps the AI tailor the response to your level of understanding.

3. Organize your prompt

Structure your prompt in a logical and organized manner. Use bullet points or numbered lists to break down complex tasks into manageable steps. This makes it easier for the AI system to follow along and generate a coherent response.

4. Use specific vocabulary

Choose precise and descriptive words that accurately convey your intent. Avoid vague or ambiguous terms that could lead to misinterpretation.

Example: On Bestever, resizing your creatives is a one-click task. For a custom fit, you can type your prompt in the AI chatbot and watch as your ad adjusts auto-magically. Ensure you use specific vocabulary for your desired dimensions.

5. Provide step-by-step instructions

Break down complex tasks into clear, step-by-step instructions. Use numbered lists or bullet points to make the steps easy to follow.

Example: "Provide a step-by-step guide on how to change a flat tire, including the following steps:

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   Park the car on a level surface and engage the parking brake.

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   Locate the spare tire, jack, and lug wrench.

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   Loosen the lug nuts on the flat tire, but do not remove them yet."

6. Encourage creativity

Use open-ended questions or prompts that allow the AI system to exercise its creativity and generate unique responses.

Example: "Write a short story about a time traveler who gets stranded in the past. Describe the challenges they face and how they adapt to their new surroundings."

7. Specify formatting and style

If you have specific formatting or style requirements, include them in your prompt to ensure the AI system generates output that meets your needs.

Example: "Write a 500-word article on the history of the Eiffel Tower, using the following format:

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  Introduction (100 words)

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  Construction and Design (200 words)

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  Cultural Significance (150 words)

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  Conclusion (50 words)"

8. Set content boundaries

Another vital prompt engineering guide point is setting content boundaries. By defining what should and should not be included, you help the AI focus on generating relevant and appropriate content.

Example: "Write a humorous, family-friendly story about a robot that learns to cook." Here, the boundaries are clear: the story should be humorous, suitable for all ages, and focus on a robot cooking.

9. Use chained prompts

Chain of thought prompting takes the idea of chaining prompts further by instructing the AI to think step-by-step. This method improves the logical consistency and accuracy of responses, particularly in complex or multi-step tasks.

When asking a complex question, you might instruct the AI to "Explain the process step by step." This approach not only guides the AI through the task but also provides transparency in how the final answer is reached.

Example: "Write an article about ChatGPT. First, give me the outline, which consists of a headline, a teaser, and several subheadings." Once the outline is complete, you can ask the AI to expand on each section, ensuring a well-structured and detailed final article.

10. Use multi-step prompts

For queries requiring comprehensive answers, multi-step prompts can break down the task into smaller parts. This approach ensures thorough responses that cover all aspects of the question.

Example: "First, explain supervised learning. Second, describe unsupervised learning. Finally, discuss reinforcement learning and its applications." This clear, step-by-step breakdown helps the AI address each concept individually and in depth.

11. Use few-shot prompting

Few-shot prompting allows the AI to learn from a few examples before generating a response. This technique is particularly useful for tasks requiring nuanced or context-specific outputs.

Provide the AI with a few examples, such as:

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  "Translate: 'The dog is playing in the garden.' -> 'Le chien joue dans le jardin.'"

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  Then ask it to "Translate: 'The cat is sitting on the mat.'"

With Bestever, you can localize your creatives to cater to your native target group in seconds. Simply open the creative you want to translate in the app and type in the AI chatbot: "Translate to [your target language]" to watch your ad change in real time. 

Conclusion

Prompt engineering is a powerful tool for getting the most out of your interactions with AI systems. By following the best practices outlined in this guide and experimenting with different prompt engineering techniques, you can craft prompts that elicit accurate, relevant, and engaging responses from language models. Remember, the key to effective prompt engineering is clarity, specificity, and a willingness to explore the creative potential of language.