Stop Regenerating AI Content: The 5-Prompt Framework That Gets It Right the First Time
Most people waste hours saying "make it better" to AI. Here's the structured feedback format that gets exceptional results on the first revision.
Hey there,
You’re wasting half your AI conversations on bad feedback
You know the drill. AI gives you something close but not quite right. You say “make it better” or “try again” and get... basically the same thing with different words.
That’s because vague feedback produces vague improvements.
Here’s what you’re getting instead: 5 copy-paste prompts that turn your feedback into precise instructions AI actually understands. No more guessing games. No more regenerating the same mediocre output five times.
You’ll learn the exact format that gets better results on the first revision, how to diagnose what’s actually wrong with AI output, and the framework that works across every AI platform.
Why most AI feedback fails (and what actually works)
AI doesn’t understand “make it punchier” or “needs more personality.” It needs structured, specific feedback that identifies gaps and provides measurable targets.
These prompts give you that structure. They turn your gut reaction into actionable instructions that AI can execute immediately, cutting your revision cycles in half.
Prompt #1: The Feedback Framework Setup
What it does: Establishes your evaluation criteria before you start revising
When to use it: At the start of any project where you’ll need multiple revisions
The Prompt:
I need you to help me give effective feedback on AI-generated content. First, establish the evaluation framework.
For [TYPE OF CONTENT], create a feedback rubric with 5-7 key criteria that matter most for quality. For each criterion:
- Define what "excellent" looks like
- Define what "needs improvement" looks like
- Give me a simple 1-5 rating scale
Format this as a table I can reference quickly.
Content type: [BLOG POST/EMAIL/SCRIPT/REPORT/etc]
Target outcome: [CONVERSIONS/ENGAGEMENT/CLARITY/PERSUASION/etc]How to use it:
Specify your content type in the placeholder
Define what success looks like for this piece
Save the rubric AI creates for consistent evaluation
Use it to score the first draft before giving feedback
Example input:
Content type: LinkedIn post
Target outcome: Engagement and shares from senior marketing professionalsWhat you’ll get: A clear rubric with specific criteria like “Hook strength,” “Value density,” “Call-to-action clarity” - each with definitions and rating scales
Pro tip: Create rubrics for your most common content types once, then reuse them to maintain consistency across all your feedback
Prompt #2: The Gap Analysis Request
What it does: Identifies specific weaknesses between the current output and your target
When to use it: After getting your first draft, but before making any changes
The Prompt:
Analyze this content against my target criteria and identify gaps.
Content: [PASTE THE AI OUTPUT]
Target criteria:
[CRITERION 1]: Should achieve [SPECIFIC STANDARD]
[CRITERION 2]: Should achieve [SPECIFIC STANDARD]
[CRITERION 3]: Should achieve [SPECIFIC STANDARD]
For each criterion, tell me:
1. Current state (what it does now)
2. Gap (what's missing or weak)
3. Specific fix needed (concrete action to close gap)
Be brutally honest. I need actionable diagnosis, not encouragement.How to use it:
Paste the AI’s first attempt
List your 3-5 most important criteria
Review the gap analysis AI provides
Use the “specific fix” suggestions in your next prompt
Example input:
Content: [Your AI-generated email draft]
Target criteria:
Subject line: Should get 35%+ open rate with value-driven promise
Opening: Should hook reader in first 15 words with specific problem
CTA: Should drive single, clear action with urgencyWhat you’ll get: A diagnosis like “Subject line is feature-focused (weak), needs benefit transformation. Opening takes 47 words to state the problem, needs compression to 15 words max.”
Pro tip: Run this on your competitor’s content too - it’s a sneaky way to reverse-engineer what’s working in your niche
Prompt #3: The Specific Revision Prompt
What it does: Translates your feedback into precise revision instructions
When to use it: When you know what’s wrong and need AI to fix specific elements
The Prompt:
Revise this content based on specific feedback.
Original: [PASTE CONTENT]
Specific revisions needed:
1. [ELEMENT TO CHANGE]: Currently [CURRENT STATE], needs to [TARGET STATE] by [METHOD]
2. [ELEMENT TO CHANGE]: Currently [CURRENT STATE], needs to [TARGET STATE] by [METHOD]
3. [ELEMENT TO CHANGE]: Currently [CURRENT STATE], needs to [TARGET STATE] by [METHOD]
Keep everything else exactly the same. Only change what I specified above.
Show me before/after for each revision so I can see the exact changes.How to use it:
Identify 2-4 specific elements that need changes
Describe the current state and the target state for each
Specify the method (shorten, strengthen, add data, remove jargon, etc.)
Review before/after to ensure precision
Example input:
Original: [Your draft]
Specific revisions needed:
1. Opening paragraph: Currently 6 sentences and 94 words, needs to be 3 sentences and 45 words max by cutting setup and leading with the core problem
2. Statistics: Currently vague "many people," needs specific numbers by adding percentages or dollar amounts
3. Tone: Currently formal and corporate, needs conversational and direct by removing passive voice and using "you" instead of "one"What you’ll get: Clean before/after comparisons showing exactly what changed, making it easy to approve or request further tweaks
Pro tip: Stack multiple revision rounds by feeding the output back through this same prompt with new specific revisions
You just got 3 prompts that turn vague feedback into precise instructions.
But here’s the thing - those handle basic revisions.
The next 2 prompts tackle what separates good content from exceptional:
Example-based correction when words fail and you need to show AI exactly what you want
Final polish that elevates content from “good enough” to publication-ready
Plus: The Master Feedback Template that combines all these techniques into one reusable framework