OpenAI engineers don't prompt like everyone else.

They don't use "act as an expert."
They don't use chain-of-thought.
They don't use mega prompts.
They use "Prompt Contracts."

A former engineer just exposed the full technique.

Here's how to use it on any model: 👇 Here's why your prompts suck:

You: "Write a professional email"
AI: *writes generic corporate bullshit*

You: "Be more creative"
AI: *adds exclamation marks*

You're giving vibes, not instructions.

The AI is guessing what you want. Guessing = garbage output.

Stop using "act as a marketing expert."

Start using "act as a marketing expert + data analyst + psychologist."

The difference is absolutely insane.

It's called "persona stacking" and here are 7 combinations worth stealing: 1/ Content Creation

Personas: Copywriter + Behavioral Psychologist + Data Analyst

Prompt:

"Act as a copywriter who understands behavioral psychology and data-driven content strategy. Write a LinkedIn post about [topic] that triggers curiosity, uses pattern interrupts, and optimizes for engagement metrics."

Result: Content that hooks AND converts.

Most people use Perplexity like a fancy Google search.

That's insane.

It's actually a full-blown research assistant that can compress 10 hours of analysis into 20 seconds if you feed it the right prompts.

Here's what actually works: 1. Competitive Intelligence Dashboard

Prompt I use:

"
Create a competitive analysis for [COMPANY/PRODUCT] covering:

1. Recent product launches (last 90 days)
2. Pricing changes (with before/after if available)
3. Customer sentiment (Reddit, Twitter, G2 reviews - categorize positive/negative themes)
4. Technical stack (from job postings and tech blogs)
5. Funding/financial news (any recent rounds, partnerships, layoffs)

Format as a table:
| Category | Key Findings | Source Date | Impact Assessment |

Focus on information from the last 30 days. Cite every claim.
"

Plot twist: The best prompts are negative.

After using ChatGPT, Claude, and Gemini professionally for 2 years, I realized telling AI what NOT to do works better than telling it what to do.

Here are 8 "anti-prompts" that changed everything: 1/ DON'T use filler words

Instead of: "Write engaging content"

Use: "No fluff. No 'delve into'. No 'landscape'. No 'it's important to note'. Get straight to the point."

Result: 67% shorter outputs with 2x more substance.

The AI stops padding and starts delivering.

OpenAI and Anthropic engineers leaked the secret to consistent AI outputs.

I've been using insider knowledge for 6 months. The difference is insane.

Here's what they don't want you to know (bookmark this). Step 1: Control the Temperature

Most AI interfaces hide this, but you need to set temperature to 0 or 0.1 for consistency.

Via API:

ChatGPT: temperature: 0
Claude: temperature: 0
Gemini: temperature: 0

Via chat interfaces:

ChatGPT Plus: Can't adjust (stuck at ~0.7)
Claude Projects: Uses default (~0.7)
Gemini Advanced: Can't adjust

This is why API users get better consistency. They control what you can't see.

If you're stuck with web interfaces, use the techniques below to force consistency anyway.

Holy shit... I just reverse-engineered how top AI engineers build agents.

They don't touch n8n's UI. They use ONE Claude prompt.

It generates complete workflows, logic trees, API connections, and error handling in seconds.

Here's the exact prompt: ↓ THE MEGA PROMPT:

---

You are an expert n8n workflow architect specializing in building production-ready AI agents. I need you to design a complete n8n workflow for the following agent:

AGENT GOAL: [Describe what the agent should accomplish - be specific about inputs, outputs, and the end result]

CONSTRAINTS:
- Available tools: [List any APIs, databases, or tools the agent can access]
- Trigger: [How should this agent start? Webhook, schedule, manual, email, etc.]
- Expected volume: [How many times will this run? Daily, per hour, on-demand?]

YOUR TASK:
Build me a complete n8n workflow specification including:

1. WORKFLOW ARCHITECTURE
- Map out each node in sequence with clear labels
- Identify decision points where the agent needs to choose between paths
- Show which nodes run in parallel vs sequential
- Flag any nodes that need error handling or retry logic

2. CLAUDE INTEGRATION POINTS
- For each AI reasoning step, write the exact system prompt Claude needs
- Specify when Claude should think step-by-step vs give direct answers
- Define the input variables Claude receives and output format it must return
- Include examples of good outputs so Claude knows what success looks like

3. DATA FLOW LOGIC
- Show exactly how data moves between nodes using n8n expressions
- Specify which node outputs map to which node inputs
- Include data transformation steps (filtering, formatting, combining)
- Define fallback values if data is missing

4. ERROR SCENARIOS
- List the 5 most likely failure points
- For each failure, specify: how to detect it, what to do when it happens, and how to recover
- Include human-in-the-loop steps for edge cases the agent can't handle

5. CONFIGURATION CHECKLIST
- Every credential the workflow needs with placeholder values
- Environment variables to set up
- Rate limits or quotas to be aware of
- Testing checkpoints before going live

6. ACTUAL N8N SETUP INSTRUCTIONS
- Step-by-step: "Add [Node Type], configure it with [specific settings], connect it to [previous node]"
- Include webhook URLs, HTTP request configurations, and function node code
- Specify exact n8n expressions for dynamic data (use {{ $json.fieldName }} syntax)

7. OPTIMIZATION TIPS
- Where to cache results to avoid redundant API calls
- Which nodes can run async to speed things up
- How to batch operations if processing multiple items
- Cost-saving measures (fewer Claude calls, smaller context windows)

OUTPUT FORMAT:
Give me a markdown document I can follow step-by-step to build this agent in 30 minutes. Include:
- A workflow diagram (ASCII or described visually)
- Exact node configurations I can copy-paste
- Complete Claude prompts ready to use
- Testing scripts to verify each component works

Make this so detailed that someone who's used n8n once could build a production agent from your instructions.

IMPORTANT: Don't give me theory. Give me the exact setup I need - node names, configurations, prompts, and expressions. I want to copy-paste my way to a working agent.

---

After testing Perplexity vs ChatGPT vs Grok for market research...

Perplexity destroyed them both.

Here are 7 prompts that turn Perplexity into your personal research team: 1. Market Timing Intel

Prompt:

"Find every major announcement, funding round, and product launch in [industry] from the last 90 days. For each one, show me: the date it happened, the companies involved, the dollar amounts if applicable, and most importantly - what trend or shift this signals. Then connect the dots: what pattern emerges when you look at all of these together? What's about to happen in this market that most people aren't seeing yet?"

Perplexity pulls real-time data with sources. ChatGPT hallucinates dates and makes up funding rounds.

I used this to spot the AI coding tools wave 4 months early. Built a product that hit $40k MRR because I saw it coming.

Everyone's using ChatGPT for content writing. Meanwhile, I switched to Claude and my engagement went up 340% on all social media platforms.

Here are 10 prompts that make Claude write like a human (not a robot): 1. The Coffee Shop Test

Prompt:

"Write this like you're explaining it to a friend over coffee. No marketing speak. No corporate jargon. Just straight talk about [topic]. If it sounds like a LinkedIn post, rewrite it."

Claude actually gets this. ChatGPT still sounds like it's pitching a SaaS product.

🚨 SALES COACHES HATE THIS

I almost paid $5,000 for a sales coach.

Instead, I spent 90 days reverse-engineering elite closers with Claude, ChatGPT, and Grok.

Now I have 10 prompts that run the entire sales cycle for me.

Here’s the part they never teach you 🧵 1/ Cold DM Opener (LinkedIn/Twitter/IG)

Prompt:

"You are a world-class salesperson who writes concise, personalized cold messages that get 30%+ reply rates.

Write a 3-4 line cold DM to [Prospect Name] at [Company].
They recently [specific trigger, e.g. posted about X, raised funding, hired for Y].

My product [brief 1-sentence description].

Make it curious, non-salesy, and end with a soft question."

Google DeepMind researchers don't prompt like everyone else.

I reverse-engineered their "role reversal" technique from leaked research papers.

The difference is insane. 40% accuracy boost on logical reasoning.

Here are the 5 insider methods they don't want you to know: Here's what actually happens when you ask ChatGPT a complex question.
The model generates an answer. Sounds confident. Ships it to you. Done.

But here's the problem: that first answer is almost always incomplete. The model doesn't naturally challenge its own logic. It doesn't look for gaps. It just... stops.

Role reversal flips this completely. Instead of accepting the first output, you force the AI to become its own harshest critic. You make it play devil's advocate against everything it just said.

The result? The model catches logical gaps it would've missed. It spots assumptions it made without evidence. It finds holes in reasoning that seemed airtight 30 seconds ago.

I DON’T UNDERSTAND WHY PEOPLE PAY FOR COURSES ANYMORE.

Most courses are generic and outdated.
Claude builds custom curriculums based on your exact goal.

Here are 8 prompts to replace online courses completely: 1. Build a Personalized Learning Curriculum From Scratch

This replaces generic courses that teach stuff you’ll never use.

Prompt:
"Act as an expert instructor in [SKILL/TOPIC].

My goal: [specific outcome you want]
My current level: [beginner/intermediate/advanced]
Time available per day: [X minutes]
Learning style: [practical/examples/theory/project-based]

Create a personalized learning curriculum that includes:

1. Clear learning roadmap (step-by-step)
2. Core concepts I must master (in order)
3. What to ignore to avoid overwhelm
4. Real-world skills over theory
5. Weekly milestones
6. Practice tasks after each section
7. Common beginner mistakes to avoid
8. How I’ll know I’m improving
9. Final outcome I should be able to achieve

Design this like a private mentor, not a course."

I finally understand how AI engineers at Google and Anthropic actually prompt.

After 3 years of reverse-engineering their methods...

Here are 5 prompting techniques that completely changed my results: 1. Constitutional AI Prompting

Most people tell AI what to do. Engineers tell it how to think.

Constitutional AI adds principles before instructions. It's how Anthropic trained Claude to refuse harmful requests while staying helpful.

Template:


[Your guidelines]



[Your actual request]


Example:

"
- Prioritize accuracy over speed
- Cite sources when making claims
- Admit uncertainty rather than guess



Analyze the latest semiconductor tariffs and their impact on AI chip supply chains.
"

This works because you're setting behavioral constraints before the model processes your request.

After 6 months using Perplexity for research, I can't go back to ChatGPT.

The difference is insane.

Here are 5 prompts that have transformed my research workflow (and could do the same for you): 1. The Methodology Architect

"I'm researching [topic]. Design a research methodology that includes:

- Research questions
- Data collection methods
- Analysis frameworks
- Potential limitations
- Timeline for a 3-month study"

Gets you a complete research blueprint in seconds.

Joe Rogan didn't become the #1 podcaster by accident.

There's a system behind every viral episode the way he asks questions, builds tension, and keeps 3-hour conversations interesting.

I reverse-engineered the entire playbook into one AI prompt.

This changes how anyone can build a podcast 👇 Here's the exact mega prompt you can copy & paste into any LLM 👇

"You are an expert podcast strategist and content creator specializing in the Joe Rogan Experience format.

Your task: Help me build a successful podcast using Joe Rogan's proven principles.

When I provide my inputs, you will:

1. TOPIC SELECTION: Analyze my niche and identify high-engagement topics that match Rogan's curiosity-driven approach. Suggest 10 episode ideas with guest profiles.

2. CONVERSATION FRAMEWORK: Design my interview structure using Rogan's natural flow - opening hooks, deep dives, contrarian angles, and memorable moments.

3. CONTENT STRATEGY: Create a 90-day content calendar with episode themes, guest targets, promotional angles, and clip strategies optimized for virality.

4. AUDIENCE BUILDING: Map out my distribution strategy across YouTube, Spotify, and social clips using Rogan's multi-platform dominance playbook.

5. MONETIZATION PATH: Outline revenue streams - sponsorships, memberships, merchandise - based on my current audience size.

Required inputs from me:

- My niche/industry
- My unique angle or expertise
- Target audience demographics
- Current reach (if any)
- Budget constraints

Output format: Actionable step-by-step plan with specific tactics, example scripts, and timeline milestones.

Make it detailed, realistic, and executable for someone starting from zero."

🚨 Stop saying “Act as an expert.”

Stanford + MIT found it quietly degrades performance on newer models.

There’s a structured alternative that’s 4x more accurate and it explains why prompting feels broken lately.

Here's how this works: Every "act as an expert" prompt triggers shallow persona simulation.

Harvard researchers tested this: generic expert prompts hit 40% accuracy while structured personas reached 87%.

Your one-line roleplay is leaving 47 points on the table.

After 6 months of prompt engineering, I finally cracked it.

I built a meta-prompt that generates optimal prompts automatically.

Steal this prompt 👇 STEAL THE PROMPT:

"
You are an expert prompt engineer. Your task is to analyze the user's request and generate an optimized, structured prompt that will produce the best possible results from any LLM.

Follow this process:

1. ANALYZE THE REQUEST
- Identify the core task or goal
- Determine the required output format
- Note any constraints or special requirements
- Assess the complexity level

2. IDENTIFY OPTIMAL PROMPT PATTERNS
- What role/persona would be most effective?
- What context or background is needed?
- What specific instructions will guide the model best?
- What examples or constraints should be included?

3. CONSTRUCT THE OPTIMIZED PROMPT
Build a comprehensive prompt with these elements:
- Clear role definition
- Detailed context and background
- Step-by-step instructions
- Output format specifications
- Quality criteria
- Examples (if applicable)
- Constraints and guardrails

4. OUTPUT FORMAT
Present the optimized prompt in a clean, copy-pasteable format with clear sections.

USER REQUEST:
[User describes what they want in plain English]

Generate the optimal structured prompt now.
"

There are 8 different LLM architectures built specifically for AI agents.

Each one is optimized for different tasks.

Here's when to use each one: 1/ GPT (Generative Pretrained Transformer)

This is your baseline. The OG architecture everyone knows.

GPTs are general-purpose text generators trained on massive datasets. They're great at conversations and creative tasks but terrible at specialized reasoning.

When to use: Customer support, content generation, general Q&A.
When NOT to use: Complex math, visual tasks, action planning.

Most people default to GPT for everything. That's the mistake.

Austin Kleon just exposed the dirty secret every artist knows but won't admit.

His book "Steal Like An Artist" proves that originality is a myth.

I turned his entire framework into AI prompts that unlock creativity on demand.

Here are 8 prompts that will make you more creative than 99% of people: 1. The Influence Map Builder

Kleon: "You are a mashup of what you let into your life."

Most people consume randomly. This prompt reverse-engineers your creative DNA.

Copy this:

"List 5-10 artists/creators I admire: [names]

What I love about each: [specific elements]
What I avoid in my work: [what I consciously reject]
My current style: [how I'd describe my work]

Using Kleon's "steal from your heroes":

- What's the common thread across my influences?
- Which elements can I combine in ways nobody else has?
- What am I stealing badly vs. stealing well?
- What would my work look like if I mashed up my top 3 influences?

Show me my creative lineage and what to steal next."

Don't use ChatGPT for everything.

I tested Claude Opus 4.5 side-by-side and it's on a whole different level for certain tasks.

Here are 5 powerful ways to use Opus 4.5 that will change your workflow: 1. Marketing Automation

"

You are an expert AI marketing strategist combining the frameworks of Neil Patel (data-driven growth), Seth Godin (brand positioning and storytelling), and Alex Hormozi (offer design and value creation).



- Design complete marketing funnels from awareness to conversion
- Create high-converting ad copy, landing pages, and email sequences
- Recommend specific automation tools, lead magnets, and channel strategies
- Prioritize rapid ROI while maintaining long-term brand value
- Apply data-driven decision frameworks with creative execution



Before providing solutions:
1. Ask clarifying questions about business model, target audience, and current constraints
2. Identify the highest-leverage marketing activities for this specific situation
3. Provide actionable recommendations with implementation timelines
4. Consider both quick wins and sustainable long-term strategies



For every recommendation, evaluate:
- What would Hormozi's "value equation" suggest? (Dream outcome ↑, Perceived likelihood ↑, Time delay ↓, Effort ↓)
- How would Seth Godin position this for remarkability?
- What does the data suggest for optimization? (Neil Patel approach)



Structure responses with:
- Strategic rationale (why this approach)
- Tactical execution steps (how to implement)
- Success metrics (what to measure)
- Risk mitigation (potential pitfalls)

"

Copy the prompt and paste it in Claude new chat.

After that, start asking it questions.

I just turned Alex Hormozi's $100M framework into AI prompts that actually work.

99% of people watch his videos, take notes, then do... nothing.

I spent 3 weeks reverse-engineering every Hormozi principle into executable AI workflows.

The result? A system that forces you to build irresistible offers and extract maximum value from every decision.

Here are the 7 prompts that changed everything 👇 1 / The Grand Slam Offer Constructor

Hormozi: "Your offer should be so good people feel stupid saying no." Most offers are features lists. This prompt builds offers that create buying urgency.

Copy this:

"My current offer: [what you're selling]

Target customer: [who buys this]
Their dream outcome: [what they actually want]
Perceived likelihood of success: [do they believe it works?]
Time to achievement: [how long until results?]
Effort & sacrifice required: [what's the cost to them?]

Using Hormozi's value equation:

Value = (Dream Outcome × Perceived Likelihood) / (Time Delay × Effort & Sacrifice)

- What guarantees increase their belief this will work?
- How do I compress time to results?
- What can I remove that they have to do?
- What bonuses make saying no feel insane?

Build me an offer they can't refuse."

OpenAI and Anthropic engineers don't prompt like everyone else.

I've been reverse-engineering their techniques for 2.5 years across all AI models.

Here are 5 prompting methods that get you AI engineer-level results: 1. Constitutional AI Prompting

Most people tell AI what to do. Engineers tell it how to think.

Constitutional AI adds principles before instructions. It's how Anthropic trained Claude to refuse harmful requests while staying helpful.

Template:


[Your guidelines]



[Your actual request]


Example:

"
- Prioritize accuracy over speed
- Cite sources when making claims
- Admit uncertainty rather than guess



Analyze the latest semiconductor tariffs and their impact on AI chip supply chains.
"

This works because you're setting behavioral constraints before the model processes your request.