The ROSES Framework for prompt design is a powerful tool for anyone looking to harness the full potential of AI-powered language models. Whether you’re a beginner or experienced user, this framework offers a clear and practical way to craft effective prompts that guide AI to deliver relevant and precise responses. By focusing on five key elements—Role, Objective, Scenario, Expected solution, and Steps—ROSES ensures your instructions are thorough, structured, and aligned with your goals. In this article, we’ll explore each part of the framework in detail, explain how they connect, and provide examples to help you write prompts that get the best results.
🌹 ROSES Framework for Prompt Design
R — Role
Define who the AI should act as.
Example: “Act as a Product Manager” or “Act as a Creative Copywriter.”
This helps the model adopt the right tone, expertise, and perspective.
O — Objective
Clarify what you want to achieve.
Example: “Explain the concept of process flow in simple terms” or “Generate a user-friendly app onboarding message.”
This gives direction and keeps the AI focused on your goal.
S — Scenario
Describe the situation or context in which the task happens.
Example: “You are preparing a project report for stakeholders who are not technical.”
The better the context, the more relevant and accurate the response.
E — Expected Solution
Specify what kind of output you expect.
Example: “Provide a 3-paragraph explanation with examples” or “Give a bullet-point summary.”
This sets a standard for the AI’s response structure and depth.
S — Steps
Mention how you want the AI to approach the task.
Example: “First, define the concept; second, provide an example; third, summarize the key points.”
This guides the model to follow a logical order, avoiding vague or incomplete answers.
🧠 Example Prompt using ROSES
R: Act as a senior content strategist.
O: Create a blog post introducing the ROSES Framework for prompt writing.
S: The audience is beginners learning how to write better AI prompts.
E: Output a 3-paragraph blog in a friendly, easy-to-understand tone.
S: Start with an intro, explain each part of ROSES, and end with a summary.
Defining the role: setting the AI’s perspective
The first step in the ROSES framework is Role, which means specifying who or what the AI should “act as” during the task. This direction shapes the tone, style, and expertise level of the AI’s response. For example, instructing the AI to act as a “Product manager” will produce answers focused on project timelines and stakeholder coordination, while asking it to be a “Creative copywriter” will generate more imaginative, marketing-friendly content. Defining the role prevents ambiguous or generic answers by aligning the AI’s mindset with your intended outcome.
Clarifying the objective: focusing the AI’s output
Objective refers to clearly stating what you want the AI to accomplish. This is crucial because language models perform better when they understand the purpose behind the prompt. Objectives can range widely—from explaining a complex concept clearly to generating catchy social media posts. For instance, “Explain the concept of process flow in simple terms” tells the AI to simplify, while “Generate a user-friendly app onboarding message” asks for a concise, engaging text. When your objective is specific, the AI will focus on meeting that goal rather than wandering off-topic.
Providing the scenario: creating relevant context
Scenario gives context or background about where and how the AI’s output will be used. This information helps the model tailor its response to suit the particular audience or situation. For example, a prompt stating “You are preparing a project report for stakeholders who are not technical” encourages the AI to avoid jargon and use plain language. The depth of the scenario directly impacts response accuracy and relevance, as well as the tone and complexity of the generated content.
Specifying the expected solution and steps
Finally, Expected solution and Steps guide the structure and process of the AI’s answer. Expected solution means telling the AI how you want the output formatted—whether it’s a list, a set number of paragraphs, or a summary. Steps outline the approach the AI should take, such as “First define the concept, then give examples, and finally summarize.” This structure reduces vague or incomplete answers by breaking down complex tasks into manageable parts, enabling the AI to deliver organized and high-quality content consistently.
How ROSES ties it all together
By combining Role, Objective, Scenario, Expected solution, and Steps, the ROSES framework creates a comprehensive prompt blueprint that communicates precisely what you need from an AI model. Below is a table summarizing each component:
| Component | Purpose | Example |
|---|---|---|
| Role | Define AI’s persona or expertise | “Act as a Creative copywriter” |
| Objective | State the goal of the task | “Generate a user-friendly app onboarding message” |
| Scenario | Provide relevant context or audience | “Preparing a report for non-technical stakeholders” |
| Expected solution | Specify output format and depth | “Provide a 3-paragraph explanation with examples” |
| Steps | Outline how the AI should approach the task | “First, define the concept; second, provide examples; third, summarize key points” |
Using ROSES helps users avoid ambiguity and improves the quality and usefulness of AI-generated content. It acts as a checklist for prompt writing, making it easier to craft clear instructions no matter the industry or application.
Final thoughts on mastering prompt design with ROSES
In summary, the ROSES framework is a practical guide for getting the best from AI language models. By deliberately setting the Role, defining your Objective, framing the Scenario, detailing the Expected solution, and defining clear Steps, you give the AI a solid foundation to produce responses that are relevant, coherent, and actionable. Whether you are creating marketing copy, technical documentation, or educational materials, ROSES offers a logical and structured way to communicate your needs to the AI effectively. Adopting this methodology can save time, reduce frustration, and significantly enhance the quality of your outputs, empowering you to confidently integrate AI into your workflows.