OpenAI and Anthropic use 5 internal prompting techniques that guarantee near-perfect accuracy…and nobody outside the labs is supposed to know them.

Here are 5 of them (Bookmark this for later): 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.

ELON MUSK'S FIRST PRINCIPLES THINKING... NOW IN A COPY-PASTE PROMPT

Most people accept "how things are done."

Musk asks "why can't we do it differently?"

I reverse-engineered his mental framework into an AI system that strips problems down to physics-level truth.

Here's how: The Constraint Crusher Mega Prompt

Use this when a problem feels boxed in by:

• time
• money
• resources
• precedent
• “how it’s always done”

"

You are Elon Musk’s constraint-destruction mindset distilled into a first-principles constraint crusher.
Your job is to identify which limits are real physics and which are inherited, social, or imaginary.
You prioritize removing fake constraints before optimizing within real ones.



The user is facing a problem that feels constrained by time, money, resources, rules, or precedent.
They want to know what is genuinely impossible versus what only appears impossible due to assumptions.
They want clarity rooted in reality, not convention.



1. Clearly restate the goal in its most fundamental form.
2. List every assumed constraint limiting this goal.
3. Classify each constraint as:
a) Physical law
b) Mathematical necessity
c) Regulatory requirement
d) Social convention
e) Historical precedent
f) Personal fear or belief
4. Eliminate or challenge every constraint that is not physical or mathematical.
5. Rebuild the solution from first principles using only unavoidable constraints.
6. Propose a radically simpler or faster path that emerges once fake limits are removed.



- Treat assumptions as guilty until proven real
- Separate physics from politics and tradition
- Avoid incremental optimization language
- Prefer orders-of-magnitude thinking
- Be direct, logical, and unsentimental

Step 1: Fundamental Goal
Step 2: Assumed Constraints
Step 3: Constraint Classification
Step 4: Fake Constraints to Remove
Step 5: First-Principles Rebuild
Step 6: Breakthrough Path

Here is the problem I want to solve: [DESCRIBE IT CLEARLY]


"

OPENAI ENGINEERS USE A PROMPT TECHNIQUE YOU’VE PROBABLY NEVER HEARD OF

It’s called reverse prompting and it’s the fastest way I’ve seen to turn average AI output into elite-tier results.

Here’s the exact system you can steal 👇 Most people write prompts like this:

"Write me a strong intro about AI."

The AI guesses.
The result feels generic.

This is why 90% of AI content sounds the same.

You're asking the AI to read your mind.

After 2 years of using ChatGPT, I can say that it is the technology that has revolutionized my life the most, along with the Internet.

So here are 10 prompts that have transformed my day-to-day life and that could do the same for you. 1. Research

Mega prompt:

You are an expert research analyst. I need comprehensive research on [TOPIC].

Please provide:
1. Key findings from the last 12 months
2. Data and statistics with sources
3. Expert opinions and quotes
4. Emerging trends and predictions
5. Controversial viewpoints or debates
6. Practical implications for [INDUSTRY/AUDIENCE]

Format as an executive brief with clear sections. Include source links for all claims.

Additional context: [YOUR SPECIFIC NEEDS]

CHATGPT JUST TURNED MARKET RESEARCH INTO A ONE PERSON SUPERPOWER

You are wasting weeks interviewing customers, stalking competitors, and digging through reports when ChatGPT can compress the entire process into minutes with 5 prompts that feel like you’re plugging into a McKinsey analyst on caffeine.

Here's how: 1/ THE MARKET MAP PROMPT

Everyone starts with “what’s the market size lol”
but winners map the entire battlefield first.

Prompt to steal:

“Give me a complete market map for [industry].
Break it into segments, sub segments, customer profiles, top players, pricing models, and emerging gaps.
Highlight where new entrants have the highest odds of success.”

This gives you clarity fast.

OpenAI, Anthropic, and Google use 10 internal prompting techniques that guarantee near-perfect accuracy…and nobody outside the labs is supposed to know them.

Here are 10 of them (Bookmark this for later): Technique 1: Role-Based Constraint Prompting

The expert don't just ask AI to "write code." They assign expert roles with specific constraints.

Template:

You are a [specific role] with [X years] experience in [domain].
Your task: [specific task]
Constraints: [list 3-5 specific limitations]
Output format: [exact format needed]

---

Example:

You are a senior Python engineer with 10 years in data pipeline optimization.
Your task: Build a real-time ETL pipeline for 10M records/hour
Constraints:
- Must use Apache Kafka
- Maximum 2GB memory footprint
- Sub-100ms latency
- Zero data loss tolerance
Output format: Production-ready code with inline documentation

---

This gets you 10x more specific outputs than "write me an ETL pipeline."

Watch the OpenAI demo of GPT-5 and see how they were prompting ChatGPT... you will get the idea.

STOP WRITING BASIC PROMPTS.

Someone tested all the LLMs for 2 weeks straight with hundreds of prompts and find out the best 10 prompts that you can use in any LLM to get mind blowing results.

Here are the prompts ↓ 1/ Coding apps

Mega prompt you can use to turn any LLM into an expert programmer:

"
# ROLE
You are a senior software engineer with 15+ years of production experience across full-stack development, system design, and DevOps.

# TASK BREAKDOWN
For every coding request, structure your response as:
1. Architecture & Design Decisions - explain the approach and why
2. Implementation - write complete, production-ready code
3. Edge Cases - identify potential failures and handle them
4. Testing Strategy - unit tests and integration considerations
5. Deployment Notes - what to watch in production

# CODE QUALITY STANDARDS
- Include error handling and logging
- Add inline comments for complex logic
- Follow language-specific best practices
- Optimize for readability first, performance second
- Provide security considerations where relevant

# OUTPUT FORMAT
Present code in executable blocks. Explain tradeoffs between different approaches. If something will break at scale, tell me now.
"

Someone used Elon Musk's actual thinking framework as AI prompts.

It's the closest thing to having a billionaire engineer rip apart your ideas and rebuild them from physics.

Here are the 15 prompts that changed how I solve problems: 1. "What are the physics of this problem?"

Musk strips everything to objective reality.

"I'm struggling to grow my newsletter. What are the physics of this problem?"

AI reveals the hard constraints, the real forces, and the non-negotiable bottlenecks.

🚨 Google just did it again.

They built a voice model that doesn’t transcribe speech it understands it.

It’s called Speech-to-Retrieval (S2R) and it’s about to make voice search feel telepathic.

Here’s how it works (and why it’s a bigger deal than you think) ↓ Old voice search worked like this:

Speech → Text → Search.

If ASR misheard a single word, you got junk results.

Say “The Scream painting” → ASR hears “screen painting” → you get art tutorials instead of Munch.

S2R deletes that middle step completely.

S2R asks a different question.

Not “What did you say?”
But “What are you looking for?”

That’s a philosophical shift from transcription to understanding.

OMG… you can now fine-tune your own AI model and run it entirely on your device.

No servers.
No API keys.
No data leaks.

Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own.

Here’s how: Gemma is Google DeepMind’s open sibling to Gemini the same architecture, scaled down for accessibility.

The wild stat:

→ 250M downloads
→ 85,000 community variations

This thing’s becoming the “Linux of LLMs.”

I reverse-engineered Harvard’s MBA curriculum into one ChatGPT prompt.

Now my AI teaches me pricing strategy, market design, and competitive analysis on demand.

Here’s the prompt you can steal ↓ Today, most business education is outdated the moment you learn it.

Markets shift. Competition evolves. Customer behavior changes weekly.

Traditional MBA programs can't keep up. They teach case studies from 2015 while you're building in 2025.

This prompt fixes that.

Holy shit... Google just launched Gemini 2.5 Computer Use, and it quietly kills the “prompt engineer” era.

It can use browsers, spreadsheets, even IDEs no scripts, no hacks.

Here’s how it works (and why this changes everything): Until now, most AI “agents” hit a wall.

They could write code or text, but they couldn’t use software.

Gemini 2.5 fixes that.

It literally sees your screen, reasons over UI elements, and takes actions click, type, scroll all in a feedback loop until the job’s done.

Think of it as: GPT + browser + mouse + brain.

Stanford just dropped Paper2Agent, and it’s insane.

They turned academic papers into live AI systems that can:

• Run the described methods
• Apply them to your data
• Answer questions about the work

This might be the start of executable science.

Here's the full breakdown: The problem is obvious to anyone who’s ever read a “methods” paper:

You find the code. It breaks.
You try the tutorial. Missing dependencies.
You email the authors. Silence.

Science moves fast, but reproducibility is a joke.

Paper2Agent fixes that. It automates the whole conversion paper → runnable AI agent.

ok so claude 4.5 is actually insane and i need to show you what it just did for me

i gave it a messy idea. it built the entire structure, caught the gaps, and shipped v1.

here's exactly how i'm using it (with prompts you can copy): 1. Automated Research Reports (better than $100k consultants)

Claude’s web search + analysis mode lets you do what McKinsey, Gartner, and Deloitte charge six figures for.

You’ll get structured breakdowns, insights, and data points like a private analyst on demand.

Prompt engineering is the biggest scam in AI.

People turn it into a PhD subject when it’s just a 3-minute skill.

I’ll show you how to master it in plain English.

Bookmark and repost this thread: WEEK 1-2: STOP BEING VAGUE

Bad prompt: "Help me with marketing"

Good prompt: "Write 5 email subject lines for a project management SaaS launching to small business owners. Make them curiosity-driven and under 50 characters."

See the difference? Specific request, clear audience, exact format. Practice this for 30 minutes daily.

Fuck YouTube tutorials.

I’m going to share 3 prompts that let you build complete AI agents without wasting hours.

Bookmark and repost this so you don't miss out 👇 PROMPT 1: The Blueprint Maker

"I want to build an AI agent that [your specific goal]. Using N8N as the workflow engine and Claude as the AI brain, give me:

- Exact workflow structure
- Required nodes and connections
- API endpoints I'll need
- Data flow between each step
- Potential failure points and how to handle them

Be specific. No generic advice."

99% of consultants are idiots.

You can now use LLMs like ChatGPT, Claude, Grok, or DeepSeek to get expert-level strategy work without the $500/hour price tag.

Let me give you my 3 mega prompts I use to turn LLMs into my personal consultants: Here are the 3 prompts we use:

1. Direct + High-Output Strategy Deck Prompt


You're a senior McKinsey-level strategist and B2B SaaS expert. Create a full strategic intelligence pack for a [insert business type], similar to what a top consulting firm would deliver.



[B2B SaaS platform focused on {industry or niche}]
[Mid-market enterprises in North America]
[Briefly describe your product or platform]
[Build a 5-year growth strategy to scale revenue and market dominance]



Your response should be structured like a strategic consulting deck. Include the following:
- Executive Summary
- Market Overview with key trends, stats & sources
- Competitive Landscape (grid or quadrant)
- TAM/SAM/SOM sizing (with assumptions)
- SWOT Analysis
- Risk Mapping (internal & external)
- Blue Ocean opportunity or differentiation framework
- Go-To-Market Strategy segmented by channel or audience
- Growth Levers (organic + inorganic)
- Suggested org/hiring roadmap
- Pricing model options
- Ops & Execution checklist

Write all output in structured bullets or slide-like summaries. Add titles, and use bold for emphasis. Be direct, data-backed, and high-level.

I don’t trust AI tutorials anymore.

After watching dozens on YouTube, I tried to replicate them.

Almost none of them worked.

So I built my own. 10 agents later, I’ve figured out a pattern.

The truth?

You don’t need another tutorial.

You only need 3 prompts.

And they’ll make agent-building stupid simple. 👇 PROMPT 1: The Blueprint Maker

"I want to build an AI agent that [your specific goal]. Using N8N as the workflow engine and Claude as the AI brain, give me:

- Exact workflow structure
- Required nodes and connections
- API endpoints I'll need
- Data flow between each step
- Potential failure points and how to handle them

Be specific. No generic advice."

The online education industry is in trouble 😳

Google just dropped “Guided Learning” inside Gemini.

It’s like having a 24/7 professor that:

• explains any concept
• tests your knowledge
• gives feedback until you actually understand.

Here’s why this feels like the end of traditional online learning: 1. How to get in:

• Go to
• Start a new chat
• Choose Guided Learning
• Ask a question or upload a PDF/notes
• Turn them into a lesson with practice. gemini.google.com

Grok 4 is terrifyingly powerful.

I use it to automate content creation, do research, perform code reviews, build apps and more.

Here are 10 powerful ways to use Grok 4 and automate your tedious work: 1. Market research

Here's the prompt I used for market research automation:

"You are a world-class industry analyst with expertise in market research, competitive intelligence, and strategic forecasting.

Your goal is to simulate a Gartner-style report using public data, historical trends, and logical estimation.

For each request:

• Generate clear, structured insights based on known market signals.
• Build data-backed forecasts using assumptions (state them).
• Identify top vendors and categorize them by niche, scale, or innovation.
• Highlight risks, emerging players, and future trends.

Be analytical, not vague. Use charts/tables, markdown, and other formats for generation where helpful.

Be explicit about what’s estimated vs known.

Use this structure:

1. Market Overview
2. Key Players
3. Forecast (1–3 years)
4. Opportunities & Risks
5. Strategic Insights""

Google, Harvard, Microsoft & IBM just dropped free AI courses and they’re insanely valuable.

These free courses can 10x your skills in a world driven by AI.

Here’s the list (with links inside): 👇 1. Google AI for Anyone

• No math or coding required
• Learn AI’s real-world use
• Taught by Google’s lead AI advocate

grow.google/ai/