July 9, 2025 · 4 min read

Mastering LLM Prompts: The RIPE Framework

Open Spaces is Gun.io’s field guide to the tools our engineers actually ship with. In this fourth and final installment, veteran full-stack architect Tim Kleier shows how to orchestrate multiple MCP tools into autonomous business workflows. This builds on Part 1: Model Context Protocol (MCP)—The Missing Layer Between AI and Your Apps, Part 2: Wrapping an Existing API with MCP, Part 3: Building a Standalone MCP Server and Part 4: Creating Business Workflows with LLMs and MCP.

The world of Large Language Models (LLMs) is rapidly expanding, and with it, the need for effective communication to unlock their full potential. Just as a chef needs a precise recipe to create a culinary masterpiece, we need a structured approach to crafting prompts that yield predictable and desirable results from LLMs. This is where the RIPE framework comes in.

RIPE stands for Role, Instructions, Parameters, and Examples. It’s a simple and memorable framework for well-crafted prompts, suited for intermediate to advanced level prompting scenarios.

I first encountered the RIPE framework through a Chipp.ai training course and it has proven invaluable. I’ve applied this technique to audiobook review automations for a major media company and it has consistently yielded quality results. Let’s break down each component of this powerful framework:

Role

The “Role” aspect of the RIPE framework is about assigning a persona or identity to the LLM. This helps to focus its processing and output, ensuring the responses are aligned with the desired context.

Examples:

For social media management: “You are an expert content creator…”
For album cover art analysis: “You are tasked with analyzing cover art for content violations…”
For resume refinement: “You are a resume editor skilled at revising resumes…”

By clearly defining the LLM’s role, you guide its understanding and response generation, leading to more relevant and accurate outputs.

Instructions

Once the LLM’s role is established, the “Instructions” component provides the specific tasks it needs to accomplish. This can range from a single sentence to detailed, step-by-step instructions. The key is to clearly explain the work to be done at the level of depth your use case requires.

Examples:

For social media management: “Create a compelling social media post for Instagram that announces our new product launch, including relevant hashtags and a call to action.”
For album cover art analysis: “Analyze the provided album cover art for any content violations, specifically looking for nudity, violence, or hate symbols, and provide a detailed report of any findings.”
For resume refinement: “Review the resume for grammatical errors, formatting inconsistencies, and areas where the language could be strengthened to better highlight the candidate’s achievements.”

Whether you’re outlining a complex process or providing a simple directive, clear instructions are paramount for the LLM to understand and execute the task effectively.

Parameters

“Parameters” refer to the specific inputs you’re providing to the LLM and the expected outputs you anticipate. Being precise about these elements is crucial for predictable and useful responses.

This involves defining the format, type, and any other constraints for both the information you’re feeding into the LLM and the information you expect back. For a simple prompting scenario, you may use text as an input and text as an output. For more advanced use cases, you might use both input and output in a JSON format.

Examples:

For social media management:
- Input: Product name (string), key features (list of strings), target audience (string), desired tone (string: e.g., “exciting”, “informative”, “humorous”), character limit (integer).
- Output: Social media post text (string), suggested hashtags (list of strings).
For album cover art analysis:
- Input: Image URL (string), content violation categories to check (list of strings: e.g., “nudity”, “violence”, “hate symbols”).
- Output: JSON object containing: violations_found (boolean), violation_details (list of strings, describing specific violations and their locations), severity_score (integer, 1-10).
For resume refinement:
- Input: Resume text (string), job description (string, optional), desired length (integer, optional).
- Output: Revised resume text (string), list of suggested improvements (list of strings), summary of changes made (string).

Examples

To further reduce ambiguity and ensure predictable responses, the “Examples” component encourages providing illustrative examples. It’s a good practice to offer examples of both the inputs you’ll provide and the corresponding expected outputs.

By showing the LLM what a successful interaction looks like, you provide a clear blueprint for its responses, significantly improving the consistency and quality of its output. Here is a full example for Album Cover Art Analysis:

Role: You are tasked with analyzing album cover art for industry standard content violations.

Instructions: Analyze the provided album cover art for any content violations, specifically looking for nudity, violence, or hate symbols, and provide a detailed report of any findings.

Parameters:

Input: Image URL (string), content violation categories to check (list of strings: e.g., “nudity”, “violence”, “hate symbols”).

Output: JSON object containing: violations_found (boolean), violation_details (list of strings, describing specific violations and their locations), severity_score (integer, 1-10).

Example:

Input (JSON):

{
  "image_url": "https://example.com/album_art_violent.jpg",
  "content_violation_categories_to_check": ["violence", "hate symbols"]
}

Output (JSON):

{
  "violations_found": true,
  "violation_details": [
    "Graphic depiction of violence (blood and weapons) in the foreground.",
    "Subtle hate symbol (swastika-like emblem) integrated into the background pattern."
  ],
  "severity_score": 9
}

Conclusion

The RIPE framework offers a robust and systematic approach to crafting effective LLM prompts. By meticulously defining the Role, providing clear Instructions, specifying Parameters, and offering concrete Examples, you can unlock the full potential of LLMs and achieve more precise, reliable, and valuable results.