RISE Framework: Role, Input, Steps, Expectation

Some prompts require more than a simple request: they demand process, sequence, and a specific outcome. The RISE framework was created for this — to organize requests that involve structured actions, based on clear inputs and a well-defined expected result.

If you need the AI to follow a logical flow to solve a problem or execute a task with multiple steps, RISE is the ideal structure.

What Is the RISE Framework?

The RISE framework is a four-part structure that combines clarity and method, ensuring the AI processes information logically and delivers something useful. Each component helps create a focused, results-driven prompt. Let’s understand the elements and see when to use them.

Framework Components

• Role: Defines who the AI should be, like “a data analyst” or “a history teacher”, adjusting the perspective and tone.
• Input: Provides the initial information or data the AI should use, such as “a recent report” or “specific statistics”.
• Steps: Lists the steps the AI should follow, like “analyze, organize, propose”.
• Expectation: Specifies the desired outcome, such as “a clear summary” or “a detailed plan”.

These elements create a prompt that guides the AI with precision, perfect for structured tasks.

When to Use It?

RISE is ideal when you need a clear process and a specific outcome. Use it if:

• You want the AI to work with information you provide.
• You need a response organized in stages or phases.
• You seek a balance between detail and goal-oriented focus.

For example: “As a [Role], based on [Input], follow these steps: [Steps]. I expect [Expectation].”

Practical Examples of RISE in Action

To demonstrate how RISE works in practice, here are three examples applying the formula to real-life scenarios. See how it delivers organized and useful results.

Example 1: Creating a Customer Service Protocol

Context: A manager wants to standardize the initial chat support process.

As a customer experience consultant (Role), based on the most frequent customer questions listed below — such as "how to track my order", "how to exchange a product", and "payment methods" — and considering the brand’s informal and friendly communication style (Input), follow these steps: 1) define a welcome message, 2) map the ideal automated responses for these questions, 3) suggest how to escalate more complex issues to human agents (Steps). I expect a simple script for chatbot implementation and training new support agents (Expectation).

Why it works: The prompt clearly defines the role, the data considered, the process, and the final use of the material.

💡 Practical Tip

How to provide “Input” correctly?
Whenever using the RISE formula, the Input should contain information accessible to the AI at the time of execution. If it’s internal data (such as FAQ history, customer comments, or reports), summarize, list it directly in the prompt, or attach readable documents for the AI.

Example 2: Market Research for a New Product

Context: A company is evaluating the launch of a new personal finance app.

As a market research analyst (Role), based on demographic data of young adults aged 20 to 35 and financial app usage data from 2023 (Input), follow these steps: 1) define the ideal target audience, 2) list the most valued features, 3) identify competitive differentiators, 4) suggest the most promising acquisition channels (Steps). I expect a structured report with insights and positioning recommendations (Expectation).

Why it works: The prompt guides the AI through a complete, data-based process, with clarity on the expected deliverable.

Example 3: Developing an Educational Lesson Plan

Context: An educator needs to build a lesson plan to teach basic programming to beginners.

As an information technology teacher (Role), based on introductory content about programming logic and pseudocode examples (Input), follow these steps: 1) present the main concepts, 2) propose practical activities, 3) include a final challenge with annotated corrections (Steps). I expect a 60-minute lesson plan, using language accessible to beginners (Expectation).

Why it works: The role and input guide the tone, and the sequence of steps helps the AI structure a complete plan.

Tips to Get the Most Out of RISE

RISE is practical and can be adjusted to meet your specific needs. Here are some suggestions to customize it and get the best results.

Customize to Your Goal

• Role: Choose a role that matches the task. “A consultant” is more formal than “a practical guide”.
• Input: Provide useful, specific information. “Sales data” is clearer than “some ideas”.
• Steps: Adjust the number and focus. Three steps for something simple, more for complex tasks.
• Expectation: Be precise about the outcome. “Something useful” is vague, while “a guide with 3 sections” is direct.

Quick Example: “As a content strategist (Role), use this topic: ‘social media trends in 2025’ (Input) and follow: 1) Research trends, 2) Select top 3, 3) Explain impact (Steps). I expect an annotated list (Expectation).”

Start Using RISE Today

RISE is your partner for getting structured, detailed answers from an AI. It combines a defined role, clear information, and an organized process, ensuring your interactions with LLMs are productive and aligned with your goals. Whether for analysis, planning, or guides, this formula helps you achieve standout results.

🎯 Quick Summary: RISE defines who does it (Role), what to use (Input), how to do it (Steps), and the result (Expectation).

🔗 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.