The Power of Prompt Formulas

While every ChatGPT conversation is unique, certain prompt structures consistently deliver better results. These "formulas" provide frameworks you can adapt to any situation, saving time while improving output quality. This guide presents battle-tested templates for common tasks.

Think of these formulas as recipes: the basic structure stays the same, but you customize the ingredients for your specific needs. Master these patterns, and you'll communicate more effectively with ChatGPT across any domain.

The RTF Formula: Role-Task-Format

The most versatile and widely applicable formula:

Structure:
"Act as [ROLE]. [TASK description]. Format your response as [FORMAT]."

Example:
"Act as an experienced marketing copywriter. Write a product description for a sustainable bamboo water bottle targeting eco-conscious millennials. Format your response as a 150-word paragraph with a catchy headline."

Why It Works: Setting a role primes ChatGPT with relevant expertise. Clear task definition prevents ambiguity. Format specification ensures usable output.

Variations:
• Add tone: "...in a friendly, conversational tone"
• Add constraints: "...avoid jargon and keep sentences under 20 words"
• Add examples: "...similar to Apple's product descriptions"

The CTE Formula: Context-Task-Examples

Perfect when examples help define your expectations:

Structure:
"Context: [BACKGROUND]. Task: [WHAT YOU NEED]. Examples of what I'm looking for: [EXAMPLES]."

Example:
"Context: I'm a teacher creating a quiz for 8th-grade students about the American Revolution. Task: Generate 10 multiple-choice questions with 4 options each. Examples of what I'm looking for: Questions that test understanding rather than just memorization, like 'What was the PRIMARY reason colonists opposed the Stamp Act?' rather than 'In what year was the Stamp Act passed?'"

Why It Works: Context provides necessary background. Examples demonstrate quality and style expectations. This formula excels when explaining what you want is difficult.

The Problem-Solution Formula

Ideal for getting actionable advice:

Structure:
"Problem: [DESCRIBE PROBLEM]. What I've tried: [PREVIOUS ATTEMPTS]. What I need: [DESIRED OUTCOME]. Constraints: [LIMITATIONS]."

Example:
"Problem: Our email newsletter has a 12% open rate, well below industry average of 21%. What I've tried: Changing send times, A/B testing subject lines with emojis vs. without. What I need: 5 specific, actionable strategies to improve open rates. Constraints: We have a small team and limited budget, so solutions should be implementable within our current email platform."

Why It Works: Prevents redundant suggestions by sharing what you've tried. Constraints prevent impractical recommendations. Clear outcome definition focuses the response.

The Step-by-Step Formula

For complex tasks requiring detailed guidance:

Structure:
"I need to [GOAL]. Please provide step-by-step instructions for [SPECIFIC TASK]. Include [SPECIFIC REQUIREMENTS]. Format as numbered steps with explanations."

Example:
"I need to set up a CI/CD pipeline for my Node.js application. Please provide step-by-step instructions for deploying to AWS using GitHub Actions. Include code snippets for the workflow file, necessary IAM permissions, and common troubleshooting tips. Format as numbered steps with explanations for each step."

Why It Works: Breaks complex tasks into manageable steps. Explicit requirement listing ensures comprehensive coverage. Structured format aids implementation.

The Comparative Analysis Formula

For balanced evaluations and decision-making:

Structure:
"Compare [OPTION A] and [OPTION B] for [USE CASE]. Include: pros and cons of each, key differences, and a recommendation based on [CRITERIA]."

Example:
"Compare React and Vue.js for building a medium-sized e-commerce website. Include: pros and cons of each framework, key differences in learning curve, ecosystem, and performance, and a recommendation based on a team of developers with JavaScript experience but no framework experience."

Why It Works: Balanced structure prevents one-sided analysis. Specific criteria focus the comparison on what matters to you. Recommendation request drives actionable conclusion.

The Improvement Formula

For refining existing content:

Structure:
"Here is [TYPE OF CONTENT]: [PASTE CONTENT]. Please improve it by [SPECIFIC IMPROVEMENTS]. Maintain [ELEMENTS TO KEEP]. Target audience: [AUDIENCE]."

Example:
"Here is my LinkedIn summary: [paste text]. Please improve it by making it more engaging, highlighting my leadership experience more prominently, and adding a clear call to action. Maintain the professional tone and focus on tech industry experience. Target audience: Tech recruiters and hiring managers."

Why It Works: Providing existing content gives ChatGPT concrete material to work with. Specific improvement directions guide the revision. Preservation instructions prevent losing what already works.

The Expert Interview Formula

For deep dives into complex topics:

Structure:
"You are an expert in [FIELD]. I'm going to ask you questions about [TOPIC]. Please provide detailed, nuanced answers as if you were a leading authority in the field. First question: [QUESTION]"

Example:
"You are an expert in behavioral economics. I'm going to ask you questions about decision-making biases in consumer behavior. Please provide detailed, nuanced answers as if you were a leading authority in the field. First question: How does loss aversion specifically affect online purchase decisions?"

Why It Works: Expert framing encourages depth and nuance. Multi-question setup enables follow-up exploration. Interview format allows natural conversation flow.

Output Template Formula

For consistent, structured outputs:

Structure:
"Generate [CONTENT TYPE] using this exact template:\n[TEMPLATE WITH PLACEHOLDERS]\nFill in based on: [INPUT INFORMATION]"

Example:
"Generate a meeting summary using this exact template:\n\n**Meeting Title:** [title]\n**Date:** [date]\n**Attendees:** [list]\n**Key Decisions:** [bullet points]\n**Action Items:** [bullet points with owners and deadlines]\n**Next Steps:** [paragraph]\n\nFill in based on: [paste meeting notes]"

Why It Works: Exact template ensures consistent output format. Perfect for recurring tasks where consistency matters. Reduces post-processing editing time.

Chain-of-Thought Formula

For complex reasoning and analysis:

Structure:
"[QUESTION/PROBLEM]. Think through this step-by-step, showing your reasoning at each stage before reaching a conclusion."

Example:
"A company has $100,000 to invest in marketing. They can choose between social media ads ($30 CPA, 60% conversion rate to paid), Google ads ($45 CPA, 75% conversion rate), or influencer marketing ($25 CPA, 40% conversion rate, but 2x customer lifetime value). Which option maximizes ROI? Think through this step-by-step, showing your reasoning at each stage before reaching a conclusion."

Why It Works: Explicit reasoning instruction improves accuracy on complex problems. Visible thought process allows you to verify logic. Reduces errors from jumping to conclusions.

Combining Formulas

These formulas work well in combination:

RTF + Problem-Solution: "Act as a senior UX designer. Problem: Our checkout flow has a 67% abandonment rate..."

CTE + Step-by-Step: "Context: I'm launching a podcast... Task: Provide step-by-step guide for the first episode... Examples: I like the interview style of Lex Fridman..."

Expert + Chain-of-Thought: "You are an expert in financial modeling. Think through step-by-step how you would approach valuing a SaaS company..."

Conclusion

These formulas aren't rigid rules—they're starting points. Adapt them to your needs, combine them creatively, and develop your own variations. The key is providing ChatGPT with clear structure, relevant context, and specific direction.

Start with the formula that best matches your task, customize it for your situation, and iterate based on results. Over time, you'll develop an intuition for which structures work best for different situations.

Save prompts that work well for you as personal templates. Building your prompt library accelerates future work and ensures consistent quality across similar tasks. Happy prompting!