{"id":157809,"date":"2025-03-06T09:00:00","date_gmt":"2025-03-06T08:00:00","guid":{"rendered":"https:\/\/spotler.com\/?post_type=blog&p=157809"},"modified":"2025-03-06T09:00:02","modified_gmt":"2025-03-06T08:00:02","slug":"the-importance-of-a-good-ai-prompt","status":"publish","type":"blog","link":"https:\/\/spotler.com\/en-de\/blog\/the-importance-of-a-good-ai-prompt","title":{"rendered":"The importance of a good AI prompt and how to write it\u00a0"},"content":{"rendered":"

In 2025, Artificial intelligence is becoming a default tool in every marketer’s toolkit. Tools like ChatGPT, Gemini, and Midjourney are now part of our everyday work life.\u00a0However, it is still fairly easy to tell who is using AI correctly and who is not. The quality of AI-generated content depends entirely on the instructions it is given. A vague AI prompt will yield generic results, while a well-structured, detailed prompt can produce content that is precise, engaging, and useful. The process of refining these instructions is known as prompt engineering.<\/strong>\u00a0<\/p>\n

By understanding the different prompting techniques and strategies, marketers, writers, and content creators can get the most out of their AI toolkit. In this article, we’re exploring best practices for prompt engineering, from basic principles to advanced techniques that improve both efficiency and output quality. Plus, we’re testing your AI-spotting eye<\/a>.<\/p>\n

How AI understands prompts <\/h2>\n

AI does not possess human intuition. It follows patterns based on probability<\/a>. When given a prompt, it predicts what text is most likely to follow based on the data it has been trained on. If a prompt lacks specificity, the AI will generate a response that is broadly relevant but may not align with the user’s expectations. However, when a prompt is structured clearly and provides sufficient context, the AI is more likely to produce a coherent and high-quality response.\u00a0<\/p>\n

For example, a request like “write a product description<\/a>” will result in a basic, generic response. However, adding specificity, “write a compelling product description for a high-end smartwatch designed for fitness enthusiasts, focusing on advanced health tracking, sleek design, and long battery life\u201d, will guide the AI towards a more refined and useful output.\u00a0<\/p>\n

Different prompting techniques <\/h2>\n

Multiple approaches to writing prompts exist, each influencing the AI’s response in different ways. By understanding and experimenting with these techniques, users can refine their prompts to achieve better results. We’ve tested our own examples in two different AI text generators: ChatGPT<\/a><\/strong> (more creative) and Perplexity<\/a> <\/strong>(more profound).\u00a0<\/p>\n

Zero-shot prompting <\/h3>\n

Zero-shot prompting involves providing AI with a request without any prior examples or context. This approach relies on the AI’s existing training to generate an answer.\u00a0<\/p>\n

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Example: <\/h4>\n

Prompt<\/strong>: \u201cExplain why email marketing is effective.\u201d <\/p>\n

AI output:<\/strong> A general response based on broad knowledge, covering standard benefits such as cost-effectiveness and customer engagement. <\/p>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n

While this technique is useful for simple queries, it can result in generic answers that lack nuance or specificity.  <\/p>\n

Few-shot prompting <\/h3>\n

Few-shot prompting improves the AI’s performance by providing it with a few examples before making a request. This allows the AI to identify patterns and generate a response that is more aligned with the expected style or format.\u00a0<\/p>\n

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Example: <\/h4>\n

Prompt<\/strong>: \u201cHere are two examples of engaging LinkedIn posts about iOS 18: [Example 1]<\/a>, [Example 2]<\/a>. Now, write a similar post focused on the advantages of iOS 18 for email marketers.\u201d <\/p>\n

AI output:<\/strong> A response that follows the tone, structure, and engagement style of the given examples.<\/p>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n

This approach is particularly useful when consistency in tone and structure is required, such as in branded content or social media posts. <\/p>\n

Chain-of-thought prompting <\/h3>\n

For complex tasks that require reasoning or logical sequencing, chain-of-thought prompting encourages AI to break down the response step by step. Instead of providing a direct answer, the AI is guided through a structured thought process.\u00a0<\/p>\n

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Example: <\/h4>\n

Prompt<\/strong>: \u201cExplain how AI is transforming digital marketing. First, describe why AI is important for marketers. Then, outline three key ways AI is improving marketing workflows. Finally, provide an example of a company using AI successfully.\u201d <\/p>\n

AI output:<\/strong> A well-structured response that follows the requested breakdown, making the information easier to follow and more insightful. <\/p>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n

This technique is effective for analytical writing, educational content, and problem-solving tasks. <\/p>\n

Meta prompting <\/h3>\n

Meta prompting involves instructing the AI on how to think about the request before generating a response. By framing the task in a specific way, AI can be guided towards a more appropriate output.\u00a0<\/p>\n

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Example: <\/h4>\n

Prompt<\/strong>: \u201cImagine you are a technology journalist writing for a major publication. Your goal is to educate readers on the rise of AI-generated content. Use a professional yet engaging tone, include research-backed insights, and structure the article like a news feature.\u201d <\/p>\n

AI output:<\/strong> A more polished, structured response that aligns with the requested perspective and style. <\/p>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n

Role-based prompting <\/h3>\n

By assigning AI a specific role, responses can be made more contextually aware and relevant to the task at hand. <\/p>\n

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Example: <\/h4>\n

Prompt<\/strong>: \u201cYou are a seasoned copywriter for a B2B SaaS company. Write a compelling email announcing a new feature launch, keeping the tone engaging but professional.\u201d<\/p>\n

AI output:<\/strong> A response that aligns with the perspective of a professional copywriter, incorporating marketing principles and persuasive language. <\/p>\n

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\"Thumbnail\" <\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n

This approach is useful for ensuring that AI-generated content adheres to industry-specific standards and communication styles. <\/p>\n

Self-consistency prompting <\/h3>\n

AI does not always produce the same answer for identical prompts. To ensure consistency, one technique involves prompting the AI multiple times with the same request and selecting the most accurate or useful response. This technique is particularly valuable for ensuring accuracy and reliability in AI-generated research or analytical writing. Prompt the AI tool with the same request and pick what suits your needs best.<\/p>\n

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Test yourself: Can you spot the AI? <\/h2>\n

To demonstrate the impact of detailed prompting, we’ve put Google Imagen 3 to the test. Based on previous tests with different image generation tools, we’ve found Google Imagen 3 best suited for this job. So much so that we also struggled to find the AI, even though we prompted Imagen 3 ourselves. <\/p>\n

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How good are you at spotting AI-generated images?<\/h3>\n

Test your AI knowledge. We’re showing you one real photo and one generated by AI based on an AI prompt from ChatGPT. Ready?<\/strong><\/p>\n

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\nTest your skills<\/summary>\n


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How we created this test <\/h4>\n

To be fully transparent on how we created this test on AI, we’ve decided to add our step-by-step action plan to this article. Use it for context, for inspiration, or just as an FYI.  <\/p>\n

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