AI experts from Anthropic, OpenAI, and Google are using 5 prompting...
Spencer Baggins@bigaiguy
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Nov 28, 2025
~2 min read
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AI experts from Anthropic, OpenAI, and Google are using 5 prompting techniques that 99.9% of people have never heard of.
But once you learn them, your outputs will instantly level up.
Here’s how they work:
But once you learn them, your outputs will instantly level up.
Here’s how they work:
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1. Constraint-Stacking Prompts
Experts don’t ask the model to “write better.”
They stack constraints that force higher-order reasoning. The model becomes sharper because it has to satisfy multiple conditions at once.
Example:
“Rewrite this paragraph with
• a grade 5 clarity level
• 2 insights that a senior engineer would respect
• a logical chain with each sentence adding new information
• a single takeaway in the final line”
This forces the model to optimize for clarity, depth, structure, and purpose all at once.
Experts don’t ask the model to “write better.”
They stack constraints that force higher-order reasoning. The model becomes sharper because it has to satisfy multiple conditions at once.
Example:
“Rewrite this paragraph with
• a grade 5 clarity level
• 2 insights that a senior engineer would respect
• a logical chain with each sentence adding new information
• a single takeaway in the final line”
This forces the model to optimize for clarity, depth, structure, and purpose all at once.
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2. Reversal Prompts
Instead of asking the model to “give the answer,” experts ask it to predict the wrong answer first, then correct itself.
This activates contrastive reasoning.
Example:
“Give me the worst possible explanation of vector embeddings.
Then rewrite it into the best possible explanation.
Highlight exactly what changed and why.”
The model becomes self-corrective, analytical, and far more precise.
Instead of asking the model to “give the answer,” experts ask it to predict the wrong answer first, then correct itself.
This activates contrastive reasoning.
Example:
“Give me the worst possible explanation of vector embeddings.
Then rewrite it into the best possible explanation.
Highlight exactly what changed and why.”
The model becomes self-corrective, analytical, and far more precise.
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3. Perspective-Swap Prompts
Claude, Gemini, and o1 respond insanely well to “role cycling.”
You rotate the model through multiple expert lenses to widen its reasoning space.
Example:
“Explain how to improve my marketing funnel from 3 angles:
• A behavioral psychologist
• A data scientist
• A CMO
Then merge all 3 into a unified action plan.”
This fuses cognitive science, analytics, and strategic thinking into one output.
Claude, Gemini, and o1 respond insanely well to “role cycling.”
You rotate the model through multiple expert lenses to widen its reasoning space.
Example:
“Explain how to improve my marketing funnel from 3 angles:
• A behavioral psychologist
• A data scientist
• A CMO
Then merge all 3 into a unified action plan.”
This fuses cognitive science, analytics, and strategic thinking into one output.
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4. Scaffolding Prompts
This is the hidden superpower.
Instead of 1 big prompt, experts break the task into dependency steps.
The model builds its own plan before generating the answer.
Example:
“Break down all steps required to design a SaaS onboarding system.”
“For each step, list the decisions that matter.”
“Now create the final onboarding system using that plan.”
This produces architecture-level quality instead of random brainstorming.
This is the hidden superpower.
Instead of 1 big prompt, experts break the task into dependency steps.
The model builds its own plan before generating the answer.
Example:
“Break down all steps required to design a SaaS onboarding system.”
“For each step, list the decisions that matter.”
“Now create the final onboarding system using that plan.”
This produces architecture-level quality instead of random brainstorming.
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5. High-Resolution Objective Prompts
Most users give vague goals.
Experts zoom in until the objective becomes impossible to misinterpret.
Example:
“Write a landing page hero section that targets
• mid-level PMs at fintech companies
• who have failed with 3 tools before
• want a guaranteed result
Include: promise, proof, mechanism, CTA
Max 38 words.”
The model stops guessing and starts executing.
Most users give vague goals.
Experts zoom in until the objective becomes impossible to misinterpret.
Example:
“Write a landing page hero section that targets
• mid-level PMs at fintech companies
• who have failed with 3 tools before
• want a guaranteed result
Include: promise, proof, mechanism, CTA
Max 38 words.”
The model stops guessing and starts executing.
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AI is not going to take your job.
Instead, it's going to make you rich and help you build businesses online.
You need to know the best tools, and that's all.
We can help you find the best business AI tools.
Check our tools directory today:
aimarketing.directory
Instead, it's going to make you rich and help you build businesses online.
You need to know the best tools, and that's all.
We can help you find the best business AI tools.
Check our tools directory today:
aimarketing.directory
