Understanding AI Prompts
In the world of artificial intelligence, especially with tools like ChatGPT, the concept of a “prompt” plays a central role. A prompt is the text input that guides the AI’s response. The clearer and more intentional the prompt, the more accurate and helpful the result.
Prompts are not casual messages—they’re strategic instructions. Like briefing a colleague, they define what you want, how you want it, and why it matters. As AI becomes more deeply embedded in professional and creative workflows, the ability to craft effective prompts is becoming a core digital skill.
Prompt Engineering: The Foundation of AI Communication
Prompt engineering refers to the deliberate crafting of instructions to guide AI output. For example, rather than vaguely asking, “What’s the weather?”, a clearer version would be: “Give me the 5-day weather forecast for Tokyo in table format.” Defined input leads to more usable output.
To structure this process, many professionals turn to prompt frameworks. One of the most recognized is the CO-STAR model, developed by GovTech Singapore and recognized in a national AI competition.
The CO-STAR Framework
CO-STAR stands for Context, Objective, Style, Tone, Audience, and Response Format. It provides a clear structure for guiding AI output, especially when clarity and consistency are essential.
Context – Provide the background scenario
Example: “This is for a company blog about workplace productivity.”
Objective – Clearly define the task or action
Example: “Summarize the top 5 time management techniques.”
Style – Specify the writing approach (e.g., formal, narrative)
Example: “Write in a conversational and easy-to-read style.”
Tone – Indicate emotional or brand alignment (e.g., optimistic, serious)
Example: “Keep the tone motivational and practical.”
Audience – Describe who the output is for
Example: “The content is aimed at busy professionals and team leads.”
Response Format – Set the output structure or format (e.g., bullets, table, article)
Example: “Organize the techniques in a bullet list with brief explanations.”
From CO-STAR to CO-STAR PRIME: A Framework for Refinement and Control
CO-STAR PRIME builds on the foundation of CO-STAR by adding five advanced elements: Preferences, Reference, Iteration, Memory, and Expectations. While CO-STAR sets the structure, PRIME is focused on refining, guiding, and iterating the AI’s output. It adds specificity, adaptability, and control to ensure your prompt aligns with context, audience, and platform goals.
Preferences – Refine tone, style, and language constraints
Example: “Avoid technical jargon and use short paragraphs.”
Reference – Provide templates, examples, or data
Example: “Use this Forbes article as a model for structure and tone.”
Iteration – Plan for feedback-driven edits
Example: “I’ll review the first draft and request improvements.”
Memory – Link to previous prompts or context
Example: “Continue with the same format used in our last newsletter.”
Expectations – Define final output structure, length limits, and any constraints
Example: “Limit the article to 500 words, include a 2-sentence summary at the end, and do not use bullet points or first-person language.”
🧰 CO-STAR PRIME Prompt Guide for Digital Marketers
This breakdown shows how each part of the CO-STAR PRIME framework can be applied in a digital marketing campaign scenario:
Context – “We’re planning content for a skincare product launch aimed at Gen Z Instagram users.”
Objective – “Generate a 5-day social content calendar to promote the product.”
Style – “Use a casual, playful style like popular beauty influencers.”
Tone – “Keep it upbeat, confident, and inclusive.”
Audience – “Target eco-conscious young women aged 18–25.”
Response Format – “Include caption, hashtags, and visual suggestion for each day.”
Preferences – “Captions under 150 words, 3 hashtags per post, no use of ‘anti-aging’ language.”
Reference – “Use our last campaign ‘Glow Naturally’ as a stylistic model.”
Iteration – “I’ll provide feedback to revise any unclear CTAs.”
Memory – “Keep tone and voice consistent with our Q1 TikTok launch.”
Expectations – “Deliver a complete 5-post series ready for internal review.”
Why CO-STAR PRIME Matters
CO-STAR PRIME transforms prompt writing into a strategic workflow. It combines the clarity of structured setup with the power of adaptive refinement. Whether you're drafting reports, social posts, scripts, or executive briefings, this model helps you unlock AI’s full potential—on your terms.
中文摘要
在現今的人工智能（AI）時代，「Prompt（提示詞）」成為與 ChatGPT 等工具互動的關鍵。提示詞是使用者輸入的文字指令，引導 AI 產出回應。指令越清晰、目的越明確，AI 的回應就越準確、越有價值。
這類「Prompt Engineering（提示詞設計）」方法，強調有系統地引導 AI，以達致理想結果。其中，來自新加坡 GovTech 並於 GPT-4 提示詞競賽中獲獎的 CO-STAR 框架，是一個實用的起點。它涵蓋六個關鍵元素：情境（Context）、目標（Objective）、風格（Style）、語氣（Tone）、受眾（Audience）、輸出格式（Response Format），可幫助用戶清楚描述需求。
然而，單一提示往往無法覆蓋實際工作中所需的調整與互動。因此，我們提出了進階模型 CO-STAR PRIME，在原框架基礎上新增五項要素：偏好（Preferences）、參考資料（Reference）、迭代（Iteration）、記憶（Memory）與輸出期望（Expectations），進一步提升提示的靈活度與個人化。
CO-STAR PRIME 結合結構與互動，不僅適用於撰寫文章與行銷內容，更適用於策略簡報、社群計畫與產品說明等多元場景。它讓 AI 成為真正的合作夥伴，而非單次回應工具。
Keywords
AI prompts, CO-STAR PRIME, ChatGPT framework, prompt engineering, digital marketing prompts, AI content generation, interactive AI workflow, reference-based AI, advanced prompt design, AI writing strategies, GPT prompt optimization, prompt structure, feedback-based AI, language model input design
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