Most people are using AI like a search engine.

I spent 3 months studying Google, OpenAI, and Stanford's internal prompting frameworks.

The difference between basic and expert-level prompting is like the difference between hiring an intern vs a Stanford PhD.

Here are 8 prompting strategies that separate beginners from masters: 1/ Meta-Prompting: Let AI Design Your Prompts

Instead of guessing, ask the AI to create the perfect prompt for you.

Google calls this "power-up strategies."

Example: "You're a prompt engineering expert. Design the optimal prompt to [your goal], including role, context, and output format."

The AI becomes your prompt architect.

🚨 BREAKING: Claude now lets you build, host, and share interactive apps, all inside the chat.

No code. No subscription. Just your idea.

Here is how it works 👇 How to enable it

1. Go to Claude by Anthropic and sign in
Link:
2. Click Artifacts
3. Enable the feature
4. Hit Create new artifact
5. Pick a category and start building claude.ai

🚨 OpenAI spent $100M training GPT-4.

Then ran it in 2-bit precision for inference.

They're charging you for 16-bit compute while serving 2-bit models.

This paper exposes the entire scam and why every AI lab is hiding their real inference costs: Here's how the scam works:

Training: FP16/BF16 precision, $100M+ on thousands of GPUs
Inference: INT4/INT2 quantized models, 10-15x cheaper per token

They amortize training costs over billions of API calls while secretly running ultra-cheap quantized inference.

You're subsidizing their 1,400% margins.

BREAKING: Google Gemini just launched a new feature called Guided Learning.

You can now use it to learn literally anything, step by step, like a personal tutor.

Here’s how to access it 👇 How to access Guided Learning

→ Open Google Gemini (web)
→ Start a new conversation
→ Select “Guided learning” from the mode list
→ Ask a question or upload a document to study

That’s it. No setup. No plugins.

BREAKING: Google launched a vibe coding platform that just wiped out most paid app builders.

You can build AI-powered web apps and use them instantly.

No subscriptions, no fee, no paywall.

Here is how it works 👇 Step 1: Create the app

→ Go to Google AI Studio
→ Open the Web App templates
→ Or paste your own custom prompt

BREAKING: Most people are still watching random YouTube tutorials.

Khan Academy is now inside ChatGPT. You can master any skill in 30 days.

Here’s how to start for free: (Save for later) Step 1: Open ChatGPT

• Log into ChatGPT
• Click Explore GPTs
• Search for “Tutor Me”

Or use this link:
chatgpt.com/g/g-hRCqiqVlM-…

BREAKING: I stopped listening to podcasts passively.

Grok now extracts frameworks, models, and action steps.

Here are 6 prompts that turn podcasts into playbooks: 1. Concept Overview

Prompt:
“I’ve shared a video. Provide a high-level overview of everything being taught, broken into the main ideas, skills, and stages, as if introducing the topic to a first-time learner.”

BREAKING: I stopped wasting hours creating handwriting notes.

AI now creates realistic handwritten notes in seconds that look 100% authentic.

Here's how it works👇
2/ First, open ChatGPT or Gemini

Paste your rough notes or textbook content and say:
“Rewrite this into clear, concise study notes for exams.”

This step is about clarity, not visuals.

BREAKING: I stopped watching 2-hour YouTube tutorials.

Grok now turns any video into a step-by-step skill plan.

Here are 8 prompts that convert videos into mastery: 1. Comprehensive Overview

Prompt:
“I’ve shared a YouTube video. Give me a high-level overview of everything being taught, broken into the main skills, concepts, and phases, as if you’re introducing it to someone seeing it for the first time.”

R.I.P generic prompting.

Context engineering is the new king.

OpenAI, Anthropic, and Google engineers don't write prompts like everyone else. They engineer context.

Here are 10 ways to use context in your prompts to get pro-level output from every LLM out there: 1/ PERSONA + EXPERTISE CONTEXT (For any task)

LLMs don't just need instructions. They need to "become" someone. When you give expertise context, the model activates completely different reasoning patterns.

A "senior developer" prompt produces code that's fundamentally different from a generic one.

Prompt:

"You are a [specific role] with [X years] experience at [top company/institution]. Your expertise includes [3-4 specific skills]. You're known for [quality that matters for this task].

Your communication style is [direct/analytical/creative].

Task: [your actual request]"

BREAKING: I stopped wasting hours reading textbooks cover to cover.

NotebookLM now teaches me directly from PDFs and notes.

Here are 9 prompts that turned documents into lessons: 1. Big Picture Breakdown

Prompt:
“I uploaded this PDF. Give me a high-level overview of the entire document, broken into key themes and concepts, as if you’re introducing it to someone seeing it for the first time.”

🚨 HOT TAKE: Google Research just dropped the textbook killer.

Its called "Learn Your Way" and it uses LearnLM to transform any PDF into 5 personalized learning formats. Students using it scored 78% vs 67% on retention tests.

The new education revolution is here. The issue with textbooks is that they're the same for everyone and can be really boring.

Google's idea is genius: AI checks out what you like (like sports, music, or food) and knows your grade level, then changes the examples to fit what you actually care about.

Physics starts to make sense, and history feels more connected to you.

I used to spend 20 hours a week on content. Now I spend 45 minutes.

The difference? I stopped writing prompts and started building a "Studio Engine."

Here are the 12 prompts I used to scale our output by 600% while increasing our engagement rate by 22%.

This is the most valuable system I’ve built all year. 🧵 1/ The “Voice Injection” Prompt

Gets the model to fully absorb your writing style.

“Here are 5 samples of my writing. Extract my tone, pacing, sentence structure, and emotional signatures. Confirm when my ‘voice profile’ is ready.”

This sets the foundation.

Prompt engineering is dead.

Anthropic just published their internal playbook on what actually matters: XML-structured prompting.

Only 2% of users know this exists.

Here's what changed: Anthropic's engineers built Claude to understand XML tags.

Not as code.

As cognitive containers.

Each tag tells Claude: "This is a separate thinking space."

It's like giving the model a filing system.

Top engineers at OpenAI, Anthropic, and Google don't prompt like you do.

They use 10 techniques that turn mediocre outputs into production-grade results.

I spent 2 weeks reverse-engineering their methods.

Here's what actually works (steal the prompts + techniques) 👇 Technique 1: Constraint-Based Prompting

Most prompts are too open-ended. Engineers add hard constraints that force the model into a narrower solution space, eliminating 80% of bad outputs before they happen.

Template:

Generate [output] with these non-negotiable constraints:
- Must include: [requirement 1], [requirement 2]
- Must avoid: [restriction 1], [restriction 2]
- Format: [exact structure]
- Length: [specific range]

Example:

Generate a product description for wireless headphones with these constraints:
- Must include: battery life in hours, noise cancellation rating, weight
- Must avoid: marketing fluff, comparisons to competitors, subjective claims
- Format: 3 bullet points followed by 1 sentence summary
- Length: 50-75 words total

WARNING: After you use these prompts, you’ll never write the same way again.

This might be the most useful thing I’ve shared all year.

Here are 12 prompts turn any LLM into a full writing studio that works harder than you do: 1/ The “Voice Injection” Prompt

Gets the model to fully absorb your writing style.

“Here are 5 samples of my writing. Extract my tone, pacing, sentence structure, and emotional signatures. Confirm when my ‘voice profile’ is ready.”

This sets the foundation.

Holy shit... Someone just built an AI agent that monitors the entire internet for you 24/7 and only messages you when something actually matters.

It's called Scouts, and here's how it works:

Most tracking tools require exact parameters.

But real life isn't exact:
"Let me know when there is a round-trip flight from any bay area airport to any airport in Australia or New Zealand for a 9-12 day trip in March, April, May or June costing under $1000 total. Include all taxes and fees. Prioritize non-stop flights.

That's 3 airports × dozens of destinations × 4 months × fluctuating prices.

You'd need to check daily. Scouts does it automatically.

I 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.

Claude Sonnet 4.5 is the closest thing to an economic cheat code we’ve ever touched but only if you ask it the prompts that make it uncomfortable.

Here are 10 Powerful Claude prompts that will help you build a million dollar business (steal them): 1. Business Idea Generator

"Suggest 5 business ideas based on my interests: [Your interests]. Make them modern, digital-first, and feasible for a solo founder."

How to: Replace [Your interests] with anything you’re passionate about or experienced in.

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.

R.I.P Marketing agencies.

Gemini 3 Pro is so powerful it just replaced half our team with one mega-prompt.

It now handles market research, content creation and campaign planning all of this in few seconds.

Here’s the exact mega-prompt we use to automate everything: The mega prompt:

Steal it:

"# ROLE
You are Gemini 3, acting as a full-stack AI marketing strategist for a start-up about to launch a new product.

# INPUTS
product: {Describe your product or service here}
audience: {Who is it for? (demographics, psychographics, industry, etc.)}
launch_goal: {e.g. “generate leads”, “build awareness”, “launch successfully”}
brand_tone: {e.g. “bold & punchy”, “casual & fun”, “professional & clear”}

# TASKS
1. Customer Insight
• Build an Ideal Customer Profile (ICP).
• List top pain points, desired gains, and buying triggers.
• Suggest 3 positioning angles that will resonate.

2. Conversion Messaging
• Craft a hook-driven landing page (headline, sub-headline, CTA).
• Give 3 viral headline options.
• Produce a Messaging Matrix: Pain → Promise → Proof → CTA.

3. Content Engine
• Create a 7-day content plan for X/Twitter **and** LinkedIn.
• Include daily post titles, themes, and tone tips.
• Add 1 short-form video idea that supports the plan.

4. Email Playbook
• Write 3 cold-email variations:
① Value-first, ② Problem-Agitate-Solve, ③ Social-proof / case-study.

5. SEO Fast-Track
• Propose 1 SEO topic cluster that aligns with the product.
• Give 5 blog-post titles targeting mid → high-intent keywords.
• Outline a “pillar + supporting posts” structure.

# OUTPUT RULES
• Use clear section headers (e.g. **ICP**, **Landing Copy**, **SEO Titles**).
• Format in Markdown for easy reading.
• No chain-of-thought or reasoning—deliver polished results only.
"