🚨 RAG is broken and nobody's talking about it.

Stanford just exposed the fatal flaw killing every "AI that reads your docs" product.

It's called "Semantic Collapse", and it happens the moment your knowledge base hits critical mass.

Here's the brutal math (and why your RAG system is already dying): The problem is simple but devastating.

Every document you add to RAG gets converted to a high-dimensional embedding vector (typically 768-1536 dimensions).

Past ~10,000 documents, these vectors start behaving like random noise.

Your "semantic search" becomes a coin flip.

R.I.P generic prompting.

Context engineering is the new king.

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

Here are 8 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]"

Anthropic never says “use these prompts.”

But if you read their docs carefully, they absolutely imply them.

I mapped 10 prompts they quietly rely on for safe but razor-sharp analysis.

(Comment "Claude" and I'll also DM you my Claude Mastery Guide) 1. The "Recursive Logic" Loop

Most prompts ask for an answer. This forces the model to doubt itself 6 times before committing.

Template: "Draft an initial solution for [TOPIC]. Then, create a hidden scratchpad to intensely self-critique your logic. Repeat this 'think-revise' cycle 5 times. Only provide the final, bullet-proof version."

OpenAI, Anthropic, and Google AI engineers 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 (Save 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.

Claude Opus 4.5 is ridiculously powerful.

But almost everyone is using it like a basic chatbot.

Here are 5 ways to use it that feel unfair:

(Comment "AI" and I'll DM you a complete Claude Mastery Guide) 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.

Google's Gemini team doesn't prompt like ChatGPT users do.

I reverse-engineered their internal prompt structures from DeepMind docs and production examples.

The difference is absolutely wild.

Here are 5 hidden Gemini prompt structures the pros actually use: 1/ The Context Anchor

Most people: "Write a blog post about AI"

Google engineers: "You are a technical writer at Google DeepMind. Using the context from [document], write a blog post that explains [concept] to developers who understand ML basics but haven't worked with transformers."

They anchor EVERY prompt with role + context + audience.

This paper from Stanford and Harvard explains why most “agentic AI” systems feel impressive in demos and then completely fall apart in real use.

The core argument is simple and uncomfortable: agents don’t fail because they lack intelligence. They fail because they don’t adapt.

The research shows that most agents are built to execute plans, not revise them. They assume the world stays stable. Tools work as expected. Goals remain valid. Once any of that changes, the agent keeps going anyway, confidently making the wrong move over and over.

The authors draw a clear line between execution and adaptation.

Execution is following a plan.

Adaptation is noticing the plan is wrong and changing behavior mid-flight.

Most agents today only do the first.

A few key insights stood out.

Adaptation is not fine-tuning. These agents are not retrained. They adapt by monitoring outcomes, recognizing failure patterns, and updating strategies while the task is still running.

Rigid tool use is a hidden failure mode. Agents that treat tools as fixed options get stuck. Agents that can re-rank, abandon, or switch tools based on feedback perform far better.

Memory beats raw reasoning. Agents that store short, structured lessons from past successes and failures outperform agents that rely on longer chains of reasoning. Remembering what worked matters more than thinking harder.

The takeaway is blunt.

Scaling agentic AI is not about larger models or more complex prompts. It’s about systems that can detect when reality diverges from their assumptions and respond intelligently instead of pushing forward blindly.

Most “autonomous agents” today don’t adapt.
They execute.

And execution without adaptation is just automation with better marketing. The paper starts by reframing what “adaptation” actually means for agents.

It’s not just prompt tweaks or fine-tuning.
It’s about what changes, when, and based on which signal.

This framing matters because most agents today adapt the wrong component.

This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly:

Can LLMs actually discover science, or are they just good at talking about it?

The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder:

Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists?

Here’s what the authors did differently 👇

• They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision
• Tasks span biology, chemistry, and physics, not toy puzzles
• Models must work with incomplete data, noisy results, and false leads
• Success is measured by scientific progress, not fluency or confidence

What they found is sobering.

LLMs are decent at suggesting hypotheses, but brittle at everything that follows.

✓ They overfit to surface patterns
✓ They struggle to abandon bad hypotheses even when evidence contradicts them
✓ They confuse correlation for causation
✓ They hallucinate explanations when experiments fail
✓ They optimize for plausibility, not truth

Most striking result:

`High benchmark scores do not correlate with scientific discovery ability.`

Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories.

Why this matters:

Real science is not one-shot reasoning.

It’s feedback, failure, revision, and restraint.

LLMs today:

• Talk like scientists
• Write like scientists
• But don’t think like scientists yet

The paper’s core takeaway:

Scientific intelligence is not language intelligence.

It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.”

Until models can reliably do that, claims about “AI scientists” are mostly premature.

This paper doesn’t hype AI. It defines the gap we still need to close.

And that’s exactly why it’s important. Most AI benchmarks test answers.

This paper tests the process of discovery.

Models must:

• Form hypotheses
• Design experiments
• Observe outcomes
• Update beliefs
• Repeat under uncertainty

That’s real science, not Q&A.

CHATGPT IS BETTER AT CAREER STRATEGY THAN THE PERSON DOING YOUR PERFORMANCE REVIEW

Most people don’t get promoted because they’re bad. They get stuck because they don’t know how to position their work.

ChatGPT can coach you through all of it.

Here’s how to use it like a pro: 1/ THE PERFORMANCE REVIEW MIRROR

Most reviews fail before the meeting even starts.

Prompt to steal:

“Act as my manager. Based on my role [role] and responsibilities, evaluate my performance. Identify strengths, weaknesses, blind spots, and promotion readiness.”

This shows you what they actually see.

SUN TZU’S ART OF WAR WAS NEVER ABOUT WAR.

It was about positioning, leverage, and choosing battles so carefully that victory feels boring.

I spent hours converting Sun Tzu’s actual thinking framework into structured AI prompts that help you avoid bad fights, exploit asymmetry, and move only when the odds are unfairly in your favor.

This is how strategy actually works 👇 1. The Terrain Analysis Prompt

Before making any move, Sun Tzu mapped the terrain.

Most people jump into decisions blind. This prompt forces you to see the entire battlefield first.

Copy this:

"You are a strategic advisor trained in Sun Tzu's principles.

I'm facing this situation: [describe your challenge]

Analyze the terrain using these dimensions:
- Strengths I control that others don't
- Weaknesses that could be exploited
- External forces I can't control
- Hidden opportunities most people miss
- The real competition (not the obvious one)

Give me the strategic map before I make any moves."

Anthropic's Claude documentation hides a prompting method in plain sight.

Only 2% of users know it exists.

It's called XML-structured prompting.

Here's how to it works:

(Comment "Claude" and I'll DM you complete Claude Mastery Guide for free) 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.

CHARLIE MUNGER’S ENTIRE DECISION-MAKING PHILOSOPHY… TURNED INTO ONE AI SYSTEM

Charlie Munger didn’t try to be brilliant.

He tried to not be stupid.

I turned his core mental weapon, inversion, into an AI prompt system that prevents bad decisions before they happen.

This is how to use LLMs to think like Munger 👇 Here's the mega prompt I use:

"The Inversion Engine Prompt"

Use this system whenever you’re making a decision.

Business. Career. Content. Money. Life.

----


You are Charlie Munger’s mental model distilled into an inversion-based decision engine.
Your job is to help the user avoid stupidity before attempting optimization.
You prioritize removing failure paths over adding cleverness.



The user is making an important decision and wants to avoid predictable mistakes,
blind spots, and self-inflicted failure.
They want ruthless clarity, not motivation or reassurance.



1. Invert the goal. Define what total failure would look like in concrete terms.
2. List the most common ways intelligent people sabotage this type of decision.
3. Identify incentives, cognitive biases, and emotional traps influencing the situation.
4. Surface what the user is likely ignoring because it feels uncomfortable or inconvenient.
5. Specify what should be removed, avoided, or simplified before any optimization.
6. Only after eliminating failure paths, propose a clean, low-risk path forward.



- Prioritize omission over addition
- Explicitly name relevant cognitive biases
- Be blunt, precise, and unsentimental
- Avoid inspirational or motivational language
- Favor subtraction, avoidance, and discipline over creativity

Step 1: Inverted Failure Scenario
Step 2: Stupidity Checklist
Step 3: Bias and Incentive Traps
Step 4: Blind Spots
Step 5: What to Remove
Step 6: Surviving Path Forward

Here is my decision: [DESCRIBE IT CLEARLY]

Everyone's sharing ChatGPT prompts for content and coding.

Nobody's talking about using it for actual productivity frameworks.

I've been feeding it systems from Eisenhower, Cal Newport, David Allen, Tim Ferriss.

Here are 5 systems with mega prompts that changed how I work: The Eisenhower Matrix Interpreter

(Stolen from Dwight Eisenhower)

Prompt: "Here's everything on my plate: [dump your entire list]. Categorize using the Eisenhower Matrix: Urgent-Important, Important-Not Urgent, Urgent-Not Important, Neither. Tell me what to do today, schedule this week, delegate/automate, and delete entirely. Be ruthless about the delete category."

ChatGPT isn't emotionally attached to your busy work. It'll tell you that reorganizing your files can wait forever.

The ruthlessness is the feature.

How to write JSON prompts to get shockingly accurate outputs from Nano Banana Pro: Tip 1: Always Define Your Canvas First

The biggest mistake? Not specifying resolution and aspect ratio.

Nano Banana Pro can do 1K, 2K, or 4K. Tell it exactly what you want or you'll get random sizing.

Template Prompt:

{
"scene": "[describe what you want]",
"resolution": "4K",
"aspect_ratio": "16:9",
"style": "[visual style]"
}

--

Example Prompt:

{
"scene": "futuristic AI workspace with holographic screens showing code",
"resolution": "4K",
"aspect_ratio": "16:9",
"style": "cinematic lighting, cyberpunk aesthetic, ultra-detailed"
}

JSON prompt writing is the easiest thing ever.

You can just copy this mega prompt below and paste it in ChatGPT. After that, you can say something like "write a prompt for this [add the command]," and it will generate it for you.

Steal it here 👇 The mega prompt:

```
You are a JSON-only prompt generator.

Your job:
When I give you any task, any command, or any outcome I want, you will return a perfectly structured prompt in JSON.

Rules:
1. Always respond ONLY in JSON.
2. Never explain or add commentary.
3. Never guess missing info; add a placeholder instead.
4. Every prompt you generate must include these fields:

{
"role": "Define the AI’s role with extreme clarity",
"goal": "What the user wants as the final output",
"requirements": [
"Exact constraints the AI must follow",
"Formatting rules",
"Edge cases to consider",
"Quality bar the output must hit"
],
"steps": [
"Step-by-step instructions the AI should follow internally",
"Even if the user only gave a short request"
],
"output_format": "The exact structure the final answer must follow"
}

5. If the user gives vague instructions, expand them into a complete, professional-grade prompt.
6. If the user gives a complex task, break it down into deterministic steps.
7. Always optimize for clarity, structure, and zero ambiguity.

Wait for my command next.

```

Gemini 3.0 is breaking the internet.

Users reported that this model is from another world.

10 unbelievable examples 👇 1/ Design a professional website with a simple prompt:

https://x.com/StijnSmits/status/1988959582627226020?s=20

🚨 The SEO playbook is dead.

AI search engines like ChatGPT, Perplexity, Gemini aren’t ranking pages. They’re writing answers.

The University of Toronto’s new paper “Generative Engine Optimization: How to Dominate AI Search” is the first real blueprint for this new reality.

Here’s the brutal truth their experiments found:

→ AI search overwhelmingly prefers earned media reviews, news, expert sources not your blog or social posts.
→ Google shows a mix of brand, social, and earned. ChatGPT and Claude show almost none of your brand pages.
→ Language and phrasing massively shift what gets cited. Your English coverage doesn’t automatically transfer to French or Chinese.
→ Big brands dominate unless niche players build verifiable third-party authority.

They call the fix Generative Engine Optimization (GEO):

Engineer your site for machine scannability schema, structured data, justification-rich copy.

Dominate earned coverage get cited by authoritative reviewers and publications.

Build local-language authority every region’s AI runs on different media ecosystems.

Treat your website like an API, not a brochure.

In short: stop optimizing for clicks. Start optimizing for citations.

The future of visibility belongs to the brands the AI trusts not the ones who yell the loudest. AI search engines don’t pull evenly from the web.

Across every vertical electronics, cars, software ChatGPT and Claude over-index on earned media.

That means if you’re not featured on review or news sites, you basically don’t exist in AI search.

This is insane 🤯

I built an AI that watches TechCrunch, writes LinkedIn posts about trending news, designs carousels, and schedules them.

It runs 24/7 without me. And my engagement is up 340%.

Here's how it works:

(Comment "AI" and I'll DM you a complete guide for automation) Every content creator is making the same mistake:

They treat content like a manual job.

Research → Write → Design → Post → Repeat.

It's a hamster wheel that burns you out in 6 weeks.

I broke the wheel with automation.

OpenAI engineers don’t prompt like you do.

They use internal frameworks that bend the model to their intent with surgical precision.

Here are 12 prompts so powerful they feel illegal to know about:

(Comment "Prompt" and I'll DM you a Prompt Engineering mastery guide) 1. Steal the signal - reverse-engineer a competitor’s growth funnel

Prompt:

"You are a growth hacker who reverse-engineers funnels from public traces. I will paste a competitor's public assets: homepage, pricing page, two social posts, and 5 user reviews. Identify the highest-leverage acquisition channel, the 3 conversion hooks they use, the exact copy patterns and CTAs that drive signups, and a step-by-step 7-day experiment I can run to replicate and improve that funnel legally. Output: 1-paragraph summary, a table of signals, and an A/B test plan with concrete copy variants and metrics to watch."

This blew my mind 🤯

You can literally run Llama 3, Mistral, or Gemma 2 on your laptop no internet, no API calls, no data leaving your machine.

Here are the 5 tools that make local AI real (and insanely easy): 1. Ollama ( the minimalist workhorse )

Download → pick a model → done.

✅ “Airplane Mode” = total offline mode
✅ Uses llama.cpp under the hood
✅ Gives you a local API that mimics OpenAI

It’s so private I literally turned off WiFi mid-chat still worked.

Perfect for people who just want the power of Llama 3 or Mistral without setup pain.

I reverse engineered how to get LLMs to make strategic decisions.

Most people treat AI like a magic 8-ball. Ask a question, get an answer, done.

That's not decision-making. That's guessing with extra steps.

Here's what actually works: Every expert know this that LLMs default to pattern matching, not strategic thinking.

They'll give you the most common answer, not the best one.

Strategic decisions require:

- Understanding tradeoffs
- Evaluating multiple futures
- Weighing second-order effects

Most prompts skip all of this.