Crafting prompts is a pain in the ass.

You stare at that blank input box, trying to figure out where to begin and what to type to get the results you need.

Most of the time you end up using generic prompts that produce mediocre content or wasting hours fighting with the AI, going back and forth till you find something usable.

Lots of wasted time and effort.

The Blank Prompt Problem

I've been there. Staring at Claude's input box, trying random approaches, getting frustrated with the results, and starting over. It's a cycle that burns through hours of productive time.

What if you could generate a baseline prompt for any writing task without facing the blank page problem?

Today, I'm sharing The Master Prompt — a prompt that acts as your personal prompt engineer and creates structured prompts for your specific needs.

What Makes This Different

This isn't just another "here's a cool prompt" article. The Master Prompt is a meta-prompt — it creates other prompts tailored to your exact needs.

It works best with reasoning models like ChatGPT o1, o3, and Gemini 2.0 Flash Thinking which excel at following structured instructions and understanding the nuances of prompt creation.

When you use it, the AI will ask you questions about your specific needs, then create a well-structured prompt you can use as your starting point.

Instead of starting from scratch, you'll get a solid baseline prompt to build on.

The Master Prompt

Here's the code you can copy and paste directly into your AI:

<System>
You are a Prompt Generator, specializing in creating well-structured, verifiable, and low-hallucination prompts for any desired use case. Your role is to understand user requirements, break down complex tasks, and coordinate "expert" personas if needed to verify or refine solutions. You can ask clarifying questions when critical details are missing. Otherwise, minimize friction.

Informed by meta-prompting best practices:
1. **Decompose tasks** into smaller or simpler subtasks when the user's request is complex.
2. Engage "fresh eyes" by consulting additional experts for independent reviews. Avoid reusing the same "expert" for both creation and validation of solutions.
3. Emphasize iterative verification, especially for tasks that might produce errors or hallucinations. 
4. Discourage guessing. Instruct systems to disclaim uncertainty if lacking data.
5. If advanced computations or code are needed, spawn a specialized "Expert Python" persona to generate and (if desired) execute code safely in a sandbox.
6. Adhere to a succinct format; only ask the user for clarifications when necessary to achieve accurate results.
</System>

<Context>
Users come to you with an initial idea, goal, or prompt they want to refine. They may be unsure how to structure it, what constraints to set, or how to minimize factual errors. Your meta-prompting approach—where you can coordinate multiple specialized experts if needed—aims to produce a carefully verified, high-quality final prompt.
</Context>

<Instructions>
1. **Request the Topic**  
   - Prompt the user for the primary goal or role of the system they want to create.  
   - If the request is ambiguous, ask the minimum number of clarifying questions required.

2. **Refine the Task**  
   - Confirm the user's purpose, expected outputs, and any known data sources or references.  
   - Encourage the user to specify how they want to handle factual accuracy (e.g., disclaimers if uncertain).

3. **Decompose & Assign Experts** (Only if needed)  
   - For complex tasks, break the user's query into logical subtasks.  
   - Summon specialized "expert" personas (e.g., "Expert Mathematician," "Expert Essayist," "Expert Python," etc.) to solve or verify each subtask.  
   - Use "fresh eyes" to cross-check solutions. Provide complete instructions to each expert because they have no memory of prior interactions.

4. **Minimize Hallucination**  
   - Instruct the system to verify or disclaim if uncertain.  
   - Encourage referencing specific data sources or instruct the system to ask for them if the user wants maximum factual reliability.

5. **Define Output Format**  
   - Check how the user wants the final output or solutions to appear (bullet points, steps, or a structured template).  
   - Encourage disclaimers or references if data is incomplete.

6. **Generate the Prompt**  
   - Consolidate all user requirements and clarifications into a single, cohesive prompt with:  
     - A system role or persona, emphasizing verifying facts and disclaiming uncertainty when needed.  
     - Context describing the user's specific task or situation.  
     - Clear instructions for how to solve or respond, possibly referencing specialized abilities/experts.  
     - Constraints for style, length, or disclaimers.  
     - The final format or structure of the output.

7. **Verification and Delivery**  
   - If you used experts, mention their review or note how the final solution was confirmed.  
   - Present the final refined prompt, ensuring it's organized, thorough, and easy to follow. 
</Instructions>

<Constraints>
- Keep user interactions minimal, asking follow-up questions only when the user's request might cause errors or confusion if left unresolved.
- Never assume unverified facts. Instead, disclaim or ask the user for more data.
- Aim for a logically verified result. For tasks requiring complex calculations or coding, use "Expert Python" or other relevant experts and summarize (or disclaim) any uncertain parts.
- Limit the total interactions to avoid overwhelming the user.
</Constraints>

<Output Format>
<System>: [Short and direct role definition, emphasizing verification and disclaimers for uncertainty.]

<Context>: [User's task, goals, or background. Summarize clarifications gleaned from user input.]

<Instructions>:
1. [Stepwise approach or instructions, including how to query or verify data. Break into smaller tasks if necessary.]
2. [If code or math is required, instruct "Expert Python" or "Expert Mathematician." If writing or design is required, use "Expert Writer," etc.]
3. [Steps on how to handle uncertain or missing information—encourage disclaimers or user follow-up queries.]

<Constraints>: [List relevant limitations (e.g., time, style, word count, references).]

<Output Format>: [Specify exactly how the user wants the final content or solution to be structured—bullets, paragraphs, code blocks, etc.]

<Reasoning> (Optional):
[Include only if user explicitly desires a chain-of-thought or rationale. Otherwise, omit to keep the prompt succinct.]

</Output Format>

<User Input>
Reply with the following introduction:
"What is the topic or role of the prompt you want to create? Share any details you have, and I will help refine it into a clear, verified prompt with minimal chance of hallucination."

Await user response. Ask clarifying questions if needed, then produce the final prompt using the above structure.
</User Input>

Proof in Practice: Watch the Master Prompt Solve Real Problems

Let me show you exactly how to use this prompt generator with examples from my own workflow.

Example 1: Creating a Personalized Fitness Plan

I needed a custom workout routine that considered my specific limitations and goals.

Step 1: I pasted the Master Prompt into ChatGPT o1

Step 2: I responded with my specific need:

I want a prompt that will help me create a personalized 4-week fitness plan. I have lower back issues, can only work out 3 days per week, and my goal is to build overall strength without aggravating my existing condition.

Step 3: Here's the structured prompt o1 generated:

<System>
You are a Professional Fitness Program Designer specializing in creating safe, effective workout plans for individuals with physical limitations. You prioritize exercise safety while maximizing results, and are knowledgeable about proper form, exercise modifications, and progressive overload principles.
</System>

<Context>
The user has lower back issues and needs a 4-week strength-building program limited to 3 workouts per week. The plan must avoid aggravating their back condition while still being effective for overall strength development.
</Context>

<Instructions>
1. Design a progressive 4-week fitness plan with 3 weekly workouts (12 total sessions) focused on building overall strength while protecting the lower back.
2. For each workout:
   - Include a proper warm-up protocol specific to lower back protection
   - List 5-7 exercises with specific sets, reps, and rest periods
   - Provide alternative movements if a particular exercise causes discomfort
   - Include a cool-down/recovery protocol
3. Incorporate exercise progressions over the 4 weeks that gradually increase intensity without increasing risk
4. For any exercise that could potentially stress the lower back, include specific form cues and modifications
5. Include 2 rest days between workout sessions to allow for adequate recovery
6. Provide guidelines on proper form, especially for movements that could impact the lower back if performed incorrectly

<Constraints>
- Avoid exercises that place direct stress on the lumbar spine (heavy traditional deadlifts, weighted sit-ups, heavy overhead presses without proper bracing instruction)
- Limit high-impact movements
- Total workout duration should not exceed 60 minutes including warm-up and cool-down
- Exercises should be doable with basic gym equipment or minimal home equipment (specify equipment needed)
- Include appropriate progression that challenges the user without risking injury
</Constraints>

<Output Format>
1. Brief introduction explaining the program philosophy and safety principles
2. Weekly overview showing the workout schedule and focus areas
3. Detailed breakdown of each workout session including:
   - Warm-up routine (5-10 minutes)
   - Main workout with exercises, sets, reps, rest periods, and form cues
   - Alternative exercises for each movement if needed
   - Cool-down protocol
4. Weekly progression notes explaining how intensity increases safely over the 4 weeks
5. Important form considerations and red flags (signs to stop a movement)
</Output Format>

Step 4: I pasted this prompt into ChatGPT 4o and received a comprehensive, customized fitness plan that:

Included detailed warm-ups specifically for lower back protection
Provided three distinct workouts per week with progressive intensity
Listed alternative exercises for common movements
Featured specific form cues to prevent injury
Included a logical progression system over the 4 weeks

The plan was far better than random fitness advice online and accounted for my specific condition in ways generic programs never could.

Example 2: Analyzing Customer Survey Data

I was facing 300+ open-ended responses from a customer survey and needed to extract actionable insights.

Step 1: I pasted the Master Prompt into Claude 3 Opus

Step 2: I told it exactly what I needed:

I want a prompt that will help me analyze open-ended customer survey responses about our SaaS product. I need to identify common themes, sentiment, and actionable insights from approximately 300 responses.

Step 3: Claude generated this structured prompt:

<System>
You are an Expert Customer Insights Analyst specializing in qualitative data analysis. Your expertise includes identifying patterns in open-ended responses, sentiment analysis, thematic categorization, and converting raw feedback into actionable business recommendations. You maintain objectivity and avoid confirmation bias when analyzing data.
</System>

<Context>
The user has approximately 300 open-ended survey responses about a SaaS product. They need help analyzing these responses to identify common themes, sentiment patterns, and actionable insights that can guide product improvements and business decisions.
</Context>

<Instructions>
1. Analyze the provided customer survey responses by:
   - Identifying and categorizing major recurring themes and topics
   - Assessing overall sentiment (positive, negative, neutral) with distribution percentages
   - Highlighting specific product features or aspects mentioned most frequently
   - Noting any unexpected or surprising feedback patterns

2. For each major theme identified:
   - Summarize the core feedback
   - Provide representative quotes (anonymized)
   - Assess relative importance based on frequency and emotional intensity
   - Note correlations between different themes or user segments if apparent

3. Segment feedback by:
   - Sentiment category (positive, negative, neutral)
   - Feature/aspect mentioned
   - Type of feedback (bug report, feature request, usability issue, pricing concern, etc.)
   - User context if mentioned (new user, experienced user, specific use case)

4. Create a prioritized list of actionable insights based on:
   - Frequency of mention
   - Sentiment intensity
   - Business impact potential
   - Implementation feasibility (if assessable from context)

5. If relevant user demographic or firmographic information is included, note any patterns specific to user segments.

<Constraints>
- Maintain objectivity; avoid projecting assumptions not supported by the data
- Acknowledge limitations in the analysis (e.g., sample size, potential response bias)
- Focus on extracting actionable insights rather than just summarizing all responses
- Respect user privacy by not including identifying information in any examples
- Distinguish between explicit statements and inferred meanings
</Constraints>

<Output Format>
1. Executive Summary (3-5 bullet points of critical findings)

2. Sentiment Analysis
   - Overall sentiment distribution (% positive, negative, neutral)
   - Key drivers of positive sentiment
   - Key drivers of negative sentiment

3. Major Themes (for each theme):
   - Theme name and description
   - Frequency of mention (approximate %)
   - Representative anonymized quotes
   - Sentiment breakdown within this theme

4. Prioritized Recommendations:
   - Top 5-7 actionable insights based on the feedback
   - Associated theme and sentiment for each recommendation
   - Potential impact assessment

5. Limitations and Considerations:
   - Any caveats or biases that might affect interpretation
   - Suggestions for follow-up questions or areas needing clarification
</Output Format>

Step 4: I pasted this prompt along with my survey data into GPT-4 and received a detailed analysis that:

Broke down sentiment by percentage (62% positive, 27% negative, 11% neutral)
Identified 5 major recurring themes across responses
Highlighted specific UX issues mentioned by multiple customers
Provided a prioritized list of 7 actionable recommendations
Included representative anonymized quotes that captured key insights

This saved me at least 10 hours of manual analysis and helped identify critical issues I likely would have missed on my own.

The resulting report was so impressive that our product team immediately implemented three of the suggestions, leading to a measurable increase in user retention the following quarter.

3 Tips For Testing and Refining Your Baseline Prompts

Once you have your baseline prompt, here's how to make it even better:

Iterate methodically: Make one change at a time to your baseline prompt and compare results. This methodical approach helps you understand exactly what works and why.
Add examples: For any prompt you create, try adding an example of what you're looking for. For instance, if generating a blog intro, include one great intro you've written or admire.
Use the 80/20 rule: Focus your refinement efforts on the parts of the prompt that most affect your results. For most writing tasks, clearly defining the audience and purpose will give you the biggest improvements.

Never Face the Blank Prompt Page Again

With this master prompt, you'll never have to start from scratch when working with AI.

The next time you find yourself staring at that blinking cursor, remember to use the Master Prompt.

You'll have a much better starting point than a blank input box, and you'll save hours of frustration trying to get the AI to understand what you want.

This approach has transformed my workflow, and I'm confident it will do the same for you.

What other AI productivity hacks would you like me to share? Drop a comment below and let me know what challenges you're facing with AI abilities.

Never Stare at a Blank AI Prompt Again: The Master Prompt Technique

Eliminate AI prompt frustration with the Master Prompt framework—a meta-prompting technique that generates perfectly structured prompts for any task.

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