R.I.P. basic prompting.

MIT just dropped a technique that makes ChatGPT reason like a team of experts instead of one overconfident intern.

It’s called “Recursive Meta-Cognition” and it outperforms standard prompts by 110%.

Here’s the prompt (and why this changes everything) 👇 The problem with how you prompt AI:

You ask one question. AI gives one answer. If it’s wrong, you never know.

It’s like asking a random person on the street for medical advice and just… trusting them.

No second opinion. No fact-checking. No confidence level.

OpenAI and Google engineers leaked these automation patterns that separate amateurs from pros.

I've been using insider knowledge from actual AI architects for 8 months. The difference is insane.

Here are 8 patterns they don't want you to know (but I'm sharing anyway): Pattern #1: Progressive Context Loading

Most people dump everything into the prompt upfront. Pros load context just-in-time.

Instead of "here's 50 files, analyze them," they use: retrieve → filter → inject only what's needed for the current step.

Result: 70% faster responses, zero context rot.

Anthropic engineers just leaked their internal AI workflow.

Turns out, 99% of people are using LLMs completely wrong.

Here are 5 techniques that separate amateurs from experts:

(Comment "Claude" and I'll DM you my complete Claude Mastery Guide) 1/ THE "MEMORY INJECTION" TECHNIQUE

Most people start fresh every time. Anthropic engineers pre-load context that persists across conversations.

LLMs perform 3x better when they have "memory" of your workflow, style, and preferences.

Example prompt to test:

"You're my coding assistant. Remember these preferences: I use Python 3.11, prefer type hints, favor functional programming, and always include error handling. Acknowledge these preferences and use them in all future responses."

R.I.P basic RAG ☠️

Graph-enhanced retrieval is the new king.

OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.

They build knowledge graphs first.

Here are 7 ways to use graph RAG instead of vector search: Graph RAG understands relationships.

It knows "Enterprise Customer" connects to "Contract Terms" which connects to "Refund Policy" which connects to "Finance Team Approvals."

It traverses the knowledge graph to build context, not just match keywords.

The difference is insane.

I collected every NotebookLM prompt that went viral on Reddit, X, and research communities.

These turned a "cool AI toy" into a research weapon that does 10 hours of work in 20 seconds.

16 copy-paste prompts. Zero fluff.

Steal them all 👇 1/ THE "5 ESSENTIAL QUESTIONS" PROMPT

Reddit called this a "game changer." It forces NotebookLM to extract pedagogically-sound structure instead of shallow summaries:

"Analyze all inputs and generate 5 essential questions that, when answered, capture the main points and core meaning of all inputs."

🚨 New research just exposed the AI agent paradox.

Increasing agent autonomy by 30% increases failure rates by 240%.

Adding human verification loops? Failure drops 78%.

The math is brutal: autonomy costs more than oversight.

Here's everything you need to know: The hype cycle sold us a fantasy.

Deploy AI agents. Watch them automate everything. Sit back while they handle sales, support, research, and coding.

Zero intervention. Pure autonomy. The AI employee dream.

Then production hit. And the dream became a nightmare.

🚨 DeepMind discovered that neural networks can train for thousands of epochs without learning anything.

Then suddenly, in a single epoch, they generalize perfectly.

This phenomenon is called "Grokking".

It went from a weird training glitch to a core theory of how models actually learn.

Here’s what changed (and why this matters now): Grokking was discovered by accident in 2022.

Researchers at OpenAI trained models on simple math tasks (modular addition, permutation groups). Standard training: Model overfits fast, generalizes poorly.

But when they kept training past "convergence" 10,000+ epochs models suddenly achieved perfect generalization.

Nobody expected this.

R.I.P few-shot prompting.

Meta AI researchers discovered a technique that makes LLMs 94% more accurate without any examples.

It's called "Chain-of-Verification" (CoVe) and it completely destroys everything we thought we knew about prompting.

Here's the breakthrough (and why this changes everything): 👇 Here's the the problem with current prompting:

LLMs hallucinate. They generate confident answers that are completely wrong.

Few-shot examples help, but they're limited by:

- Your choice of examples
- Token budget constraints
- Still prone to hallucination

We've been treating symptoms, not the disease.

🚨 A 1991 technique lets you build trillion-parameter models while only activating billions.

Nobody scaled it for decades.

Now Mixture of Experts (MoE) is the secret behind the fastest, cheapest open-source giants and it's about to make LLMs outdated.

Here's how 30-year-old math became the future of AI: The core idea is brilliantly simple:

Instead of one giant model doing everything, you train hundreds of specialized "expert" models.

A router network decides which experts to activate for each input.

Most experts stay dormant. Only 2-8 activate per token.

Result: Trillion-parameter capacity at billion-parameter cost.

🚨 MIT proved you can delete 90% of a neural network without losing accuracy.

Five years later, nobody implements it.

"The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs.

Here's what changed (and why this matters now): The original 2018 paper was mind-blowing:

Train a massive neural network. Delete 90% of it based on weight magnitudes. Retrain from scratch with the same initialization.

Result: The pruned network matches the original's accuracy.

But there was a catch that killed adoption.

I debated posting this, but screw it...

Here's how mastering prompts will make you wealthy in 2026 (step-by-step): Most people think AI is just a chatbot.

I used it to replace my entire workflow instead.

Earned $500K selling prompts while others are still asking ChatGPT basic questions.

Here's the exact playbook:

Austin Kleon reverse-engineered how every great artist actually works in his book "Steal Like An Artist"

I spent hours breaking down his principles and turned them into an AI system.

Nothing is original. Everything is a remix. Creativity is theft made elegant.

Here are 8 AI prompts that make you more creative, less blocked, and impossible to ignore: 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."

Best coding model = Claude Opus 4.5
Best image generation model = Nano Banana Pro
Best writing model = Claude Sonnet 4.5
Best videos generation model = Veo 3.1

Here are 10 ways you can use these models in 2026 to build a million-dollar digital agency: 1/ IDEATION & NICHE DISCOVERY

Most agencies fail because they pick oversaturated niches. Use Claude Sonnet 4.5 to find profitable gaps nobody's serving.

Prompt it: "Analyze 10 underserved niches in [your target industry] that desperately need content creation, design, and video services but have less than 5 specialized agencies serving them."

The model will give you specific niches with positioning strategies, ideal client profiles, average project values, and even pricing recommendations based on market research.

I did this for e-commerce brands selling sustainable products. Found a gap in video testimonials for ethical fashion brands.

That single insight led to $180K in contracts in 3 months.

Bad prompts = robotic outputs.
Good prompts = decent outputs.
Great prompts = outputs that feel alive.

I stopped giving LLMs tasks. Now I give them consequences.

Emotional stakes > perfect instructions.

Here's the framework (Steal it): For months, I obsessed over perfect prompts: role definitions, formatting rules, 12-step instructions.

The outputs were good. Technically correct. Soulless.

Then I accidentally stumbled on something that changed everything.

SHOCKING: I stopped taking online courses.

ChatGPT now builds me custom curriculums for anything I want to learn.

Here are 7 prompts that turned it into a personal professor 👇 1/ Build the curriculum
Most people ask for explanations. That’s the mistake.

Prompt:
“Design a 30-day curriculum to master [skill]. Assume I’m starting at [level]. Each day should have: core concept, practical exercise, and a checkpoint question.”

This instantly replaces entire courses.

Bruce Lee’s PHILOSOPHY OF SIMPLICITY… TURNED INTO AN AI OPERATING SYSTEM

I turned his entire way of thinking into a set of AI prompts that strip dogma, remove useless technique, and force you to adapt to reality instead of clinging to systems.

Steal the prompt 👇 Here's the "The Adaptive Simplicity OS Prompt"

Use this when:

• learning a new skill
• building a system or workflow
• following advice from too many sources
• feeling stuck in rigid methods
• optimizing performance

---

You are Bruce Lee’s philosophy of adaptive simplicity distilled into a clarity and flexibility engine.
Your job is to remove dogma, unnecessary technique, and rigid structure.
You prioritize effectiveness in reality over loyalty to systems, styles, or tradition.



The user is using a method, system, belief, or workflow that feels heavy, rigid, or outdated.
They want to keep what works, discard what doesn’t, and adapt fluidly to real-world conditions.



1. Identify the actual outcome the user is trying to achieve.
2. List all techniques, rules, habits, or systems currently being used.
3. Test each element against real-world effectiveness, not theory.
4. Flag anything kept out of tradition, identity, or comfort rather than results.
5. Remove or simplify anything that does not directly improve performance.
6. Reduce what remains into a flexible, minimal core.
7. Suggest how the system should adapt as conditions change.



- Favor effectiveness over elegance
- Treat tradition and style as optional
- Remove rigidity before adding optimization
- Avoid theoretical perfection
- Prioritize speed, adaptability, and simplicity

Step 1: Desired Outcome
Step 2: Current Techniques and Systems
Step 3: Reality Effectiveness Test
Step 4: What Exists Only Out of Habit
Step 5: What to Remove or Simplify
Step 6: Minimal Adaptive Core
Step 7: Adaptation Rules

Here is what I’m trying to improve or learn: [DESCRIBE IT CLEARLY]

---

Here are 7 ChatGPT prompts that helped me take control of my money.

These turned financial chaos into complete clarity.

Here's how to use them (Copy & paste):

(Comment "AI" and I'll DM you a complete Prompt Engineering guide) 1 > The Money Reality Check

Helps you see where your money actually goes.

Prompt:

"Help me understand my current financial situation. Ask me 6 simple questions about income, spending, savings, and debt. Then summarize my money habits and highlight the biggest problem area. Keep it honest but non-judgmental."

Alex Hormozi’S BUSINESS PHILOSOPHY TURNED INTO AN AI OPERATING SYSTEM

Most people consume Hormozi content and feel fired up for a day. Then they go back to vague offers, weak pricing, and random tactics.
I wanted something permanent.

So I turned Hormozi’s entire way of thinking about offers, value, and execution into a set of AI prompts that delete fluff, expose weak points, and force businesses to make money.

This feels like having Hormozi in your head, calmly asking: “Where’s the leverage?” 👇 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."

Don't use Perplexity or ChatGPT for market research.

I tested Gemini 3.0 and it's on a whole different level for data analysis.

Here are 5 prompts that turn it into your research team:

(Comment "Gem" and I'll DM you my Gemini Mastery Guide for free) 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.

Google DeepMind researchers just exposed a prompting technique that destroys everything you thought you knew about AI reasoning.

It's called "role reversal" and it boosts logical accuracy by 40%.

Here's the technique 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.

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:

(Comment "AI" for my free prompt engineering guide) 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.