ROSES Framework: Role, Objective, Scenario, Expected Solution, Steps

Creating effective AI prompts requires clarity, direction, and context — and that’s exactly what the ROSES framework delivers. With five well-defined components, it’s ideal for guiding AI in more strategic, organized, and applicable responses, especially in complex, creative, or multi-step tasks.

If you need a well-structured plan, a multi-phase strategy, or a detailed step-by-step approach, ROSES helps guide the AI as clearly as possible. Let’s see how it works.

What Is the ROSES Framework?

The ROSES framework is a powerful structure that combines context, purpose, and action to create effective prompts. It’s perfect for situations that require more than a simple answer, offering a logical path for the AI to follow. Let’s understand how it works and when to use it.

Framework Components

• Role: Defines who the AI should be, such as “sales expert” or “project consultant”. This adjusts the tone and perspective of the response.
• Objective: Establishes what you want to achieve, like “increase conversions” or “organize an event”.
• Scenario: Describes the context or situation, giving the AI the necessary information to understand the big picture.
• Expected Solution: Specifies the type of result you want, like “a strategic plan” or “practical tips”.
• Steps: Details the process the AI should follow, such as “analyze, plan, execute”. This ensures a structured response.

Together, these elements create a prompt that leads the AI to deliver well-reasoned, practical, and directly applicable responses.

When to Use It?

ROSES is the right choice when you need a detailed and organized response. Use it if you want to:

• Plan projects with clear phases, such as campaigns or events.
• Solve complex problems with a step-by-step approach.
• Get results that combine strategic vision and practical execution.

For example: “As a [Role], the objective is to [Objective], in the context of [Scenario]. I expect [Expected Solution], following these steps: [Steps].”

Practical Examples of ROSES in Action

To understand how ROSES works in the real world, here are two applicable examples. Each one shows how the structure organizes ideas and delivers useful results.

Example 1: Strategy for Launching an Online Course

Context: You’re an entrepreneur looking to launch an online course on productivity but need a solid plan to attract students.

Complete prompt:
"As an educational marketing specialist (Role), create a strategy to launch an online course on productivity (Objective). The scenario is a competitive market with many similar courses available (Scenario). I expect a plan that includes promotion and engagement (Expected Solution), following these steps: 1) Identify the target audience, 2) Choose marketing channels, 3) Create an initial campaign (Steps).

Why it works: The “Role” defines the expertise, the “Objective” sets a clear goal, the “Scenario” contextualizes the challenge, the “Expected Solution” specifies the outcome, and the “Steps” guide the structure. This avoids generic responses and focuses on what matters.

Example 2: Plan to Improve Team Communication

Context: You lead a remote team facing communication breakdowns and want a plan to align everyone.

As a team management consultant (Role), develop a plan to improve communication in a remote team (Objective). The scenario involves 10 members in different time zones (Scenario). I want a solution with practical actions (Expected Solution), following these steps: 1) Assess current issues, 2) Propose tools, 3) Establish routines (Steps).

Why it works: The “Role” brings authority, the “Objective” defines success, the “Scenario” details the situation, the “Expected Solution” demands practicality, and the “Steps” organize the response. It’s a complete guide for the AI.

Tips to Get the Most Out of ROSES

ROSES is flexible and can be tailored to your specific needs. Here are some practical tips to get the best from it.

Customize to Your Goal

• Role: Choose a role that sets the right tone. “Expert” brings technical depth, while “mentor” can be more accessible. Example: “As a strategist” for business or “as a facilitator” for workshops.
• Objective: Be specific. “Improve sales” is vague; “increase sales by 20%” is clear and measurable.
• Scenario: Add relevant details. “Small team” vs. “global company in crisis” changes the AI’s approach.
• Expected Solution: Define what you want. “General ideas” is broad; “plan with deadlines” is focused.
• Steps: Adjust quantity and focus. Three steps for simple tasks, five for more complex ones.

Quick Example: “As a career coach (Role), my objective is to help a client prepare for interviews within a week (Objective). The scenario is a competitive IT job market (Scenario). I want a plan with mock practice (Expected Solution), with steps: 1) Identify common questions, 2) Create answers, 3) Test in simulations (Steps).”

These adjustments ensure ROSES adapts to your context, delivering tailored responses.

Start Using ROSES Today

The ROSES framework is your ally in turning ideas into clear, actionable plans with AI’s help. With it, you align purpose, context, and action, getting responses that truly make sense for your projects or challenges. Whether for business strategies or practical solutions, ROSES brings structure and depth to your interactions.

🎯 Quick Summary: ROSES defines who the AI is (Role), what you want to achieve (Objective), the situation’s context (Scenario), the desired outcome (Expected Solution), and the path to follow (Steps).

🔗 Want to explore more frameworks like this?
Check out the Practical Guide to Prompt Techniques, Frameworks, and Formulas for LLMs, with dozens of detailed and applicable structures for different contexts and goals, plus techniques and prompt engineering tips.

📘 Bonus tip:
Download the free eBook “Prompt Engineering Unveiled”, featuring easy explanations, practical examples, and strategies ranging from basic to advanced to master communication with AI.