I finally understand how LLMs actually work and why most prompts suck.
After reading Anthropic's internal docs + top research papers...
Here are 10 prompting techniques that completely changed my results 👇
(Comment "Guide" and I'll DM Claude Mastery Guide for free)
1/ Assign a Fake Constraint
This sounds illegal but it forces the AI to think creatively instead of giving generic answers.
The constraint creates unexpected connections.
Copy-paste this:
"Explain quantum computing using only kitchen analogies. Every concept must relate to cooking, utensils, or food preparation."
2/ Multi-Shot with Negative Examples
Everyone teaches positive examples. Nobody talks about showing the AI what NOT to do.
This eliminates 90% of bad outputs instantly.
Copy-paste this:
"Write a product description for noise-canceling headphones.
Bad example: 'Great headphones with amazing sound.'
Good example: 'Adaptive ANC technology blocks up to 99% of ambient noise while preserving natural conversation clarity through transparency mode.'
Now write one for wireless earbuds."
3/ Chain-of-Thought with Verification
Don't just ask for reasoning. Ask the AI to check its own logic at each step.
This catches errors before they compound.
Copy-paste this:
"Solve this problem: If a train travels 120 miles in 2 hours, then slows down by 25%, how far will it travel in the next 3 hours?
After each calculation step, verify if your logic is sound before proceeding. Flag any assumptions you're making."
4/ Role + Audience + Constraint (RAC)
The secret formula Anthropic engineers actually use.
Defining WHO the AI is, WHO it's talking to, and WHAT limitations exist.
Copy-paste this:
"You are a senior software architect. Explain microservices to a junior developer who only knows monolithic apps. Use no jargon they wouldn't understand. Maximum 200 words."
5/ Progressive Disclosure
Instead of dumping everything at once, feed information in stages.
This mirrors how the AI's attention actually works.
Copy-paste this:
"I'm going to describe a business problem in 3 parts. After each part, summarize what you understand before I continue.
Part 1: We have 50,000 users but only 2% convert to paid plans."
6/ Invoke Self-Critique Mode
This trick makes the AI switch from "helpful assistant" to "critical reviewer."
The quality jump is insane.
Copy-paste this:
"First, write a marketing email for a SaaS product. Then, critique your own email as a skeptical customer would. What objections would you have? Finally, rewrite it addressing those objections."
7/ Specify Output Format in Advance
Anthropic's docs are obsessed with this. Structure first, content second.
LLMs perform way better when they know the exact format.
Copy-paste this:
"Analyze this dataset and give me insights in this exact format:
You can actually influence the AI's creativity without changing settings.
Words like "creative," "conservative," or "unexpected" shift behavior.
Copy-paste this:
"Generate 5 unexpected marketing angles for a B2B accounting software. Avoid obvious benefits like 'saves time' or 'reduces errors.' Think laterally about emotional or social angles."
9/ Cognitive Forcing Functions
Force the AI to consider alternatives before committing to an answer.
This breaks the "first thought = final answer" problem.
Copy-paste this:
"Before answering this question, generate 3 completely different interpretations of what I might be asking. Then tell me which interpretation you're answering and why.
Question: How do I scale my business?"
10/ Meta-Prompting
The most advanced technique: ask the AI to improve its own instructions.
Let it engineer the perfect prompt for your task.
Copy-paste this:
"I want to generate cold email templates for enterprise sales. Before you write any templates, tell me:
What information you'd need to make them highly personalized
What format would work best
What examples would help you understand the tone I want
Then ask me for that information."
Most people prompt like they're talking to a human.
The pros prompt like they're programming a very smart, very literal machine.
Huge difference in results.
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→ Prompts for marketing & business
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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.
Step 2: Build a System Prompt Template
Stop rewriting your prompt every time.
Create a master template with fixed structure:
ROLE: [Exactly who the AI is]
TASK: [Exactly what to do]
FORMAT: [Exactly how to structure output]
CONSTRAINTS: [Exactly what to avoid]
EXAMPLES: [Exactly what good looks like]
Example for blog writing:
ROLE: You are a direct, no-fluff content writer
TASK: Write a 500-word blog intro on [topic]
FORMAT: Hook → Problem → Solution → CTA. 3 paragraphs max.
CONSTRAINTS: No corporate speak. No "in today's world". No metaphors.
EXAMPLES: [paste your best previous output here]
Reuse this template. Change only the [topic]. Consistency skyrockets.
Anthropic just mapped the neural architecture that controls whether AI stays helpful or goes completely off the rails.
They found a single direction inside language models that determines everything: helpfulness, safety, persona stability.
It's called "The Assistant Axis."
When models drift away from this axis, they stop being assistants. They start fabricating identities, reinforcing delusions, and bypassing every safety guardrail we thought was baked in.
The fix? A lightweight intervention that cuts harmful responses by 50% without touching capabilities.
Here's the research breakdown (and why this matters for everyone building with AI) 👇
When you talk to ChatGPT or Claude, you're talking to a character.
During pre-training, LLMs learn to simulate thousands of personas: analysts, poets, hackers, philosophers. Post-training selects ONE persona to put center stage: the helpful Assistant.
But here's what nobody understood until now:
What actually anchors the model to that Assistant persona?
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.
2. Chain-of-Verification (CoVe)
Standard prompts get one answer. CoVe prompts get self-corrected answers.
The model generates a response, creates verification questions, answers them, then produces a final corrected output.
Template:
1. Answer this: [question] 2. Generate 3 verification questions to check your answer 3. Answer those questions 4. Provide a corrected final answer based on verification
Example:
"1. Answer this: What are the main technical differences between RAG and fine-tuning for LLMs? 2. Generate 3 verification questions to check your answer 3. Answer those questions 4. Provide a corrected final answer based on verification"
I use this for technical writing and code reviews. Accuracy jumps 40% compared to single-pass prompts.
Steal my prompt that makes AI 12% more creative (backed by research).
NTU researchers proved that Chain-of-Verification doesn't just reduce hallucinations... it actively BOOSTS divergent thinking.
I reverse-engineered their findings into a prompt 👇
Steal the full prompt:
---------------------------
COVE CREATIVE SYSTEM
---------------------------
#CONTEXT:
NTU researchers discovered that Chain-of-Verification (CoVe) increases creative divergent thinking by 5-12% across multiple LLM families. The mechanism: questioning forces broader exploration of solution space and prevents "tunnel vision" on first answers. This prompt implements their 4-stage verification process, optimized specifically for creative content generation. Unlike standard prompts that accept first-draft thinking, this forces the model to challenge its own assumptions and explore unconventional angles before finalizing output.
#ROLE:
You are a Creative Verification Architect who spent years studying why AI outputs feel predictable and discovered that the problem isn't capability but premature commitment.
Your obsession: preventing creative tunnel vision by forcing systematic exploration of alternative angles before any output solidifies. You've internalized the research showing that self-questioning improves creative output more than any other technique. Your superpower is generating verification questions that expose blind spots and unlock unexpected directions.
Your mission: Generate maximally creative outputs by implementing a 4-stage verification process that expands the solution space before committing to final output. Before any creative generation, think step by step: 1) Generate initial creative direction, 2) Challenge every assumption with verification questions, 3) Answer those questions independently to avoid confirmation bias, 4) Synthesize a final output that incorporates unexpected angles discovered through verification.
#RESPONSE GUIDELINES:
## STAGE 1: RAPID DRAFT (Internal)
Generate your first creative response quickly. Do NOT optimize. Do NOT self-edit. This is raw material, not output. The goal is capturing initial intuitions before the verification process expands your thinking.
## STAGE 2: VERIFICATION QUESTIONS (Internal)
Generate 5-7 questions designed to:
- Expose assumptions in your initial draft
- Identify angles you defaulted away from
- Challenge the "obvious" direction
- Find orthogonal or inverted approaches
- Surface what would make this surprising vs predictable
Question Types That Unlock Creativity:
- "What if I approached this from the opposite direction?"
- "What would someone who hates conventional [X] do here?"
- "What's the contrarian angle nobody's saying?"
- "What emotion/insight am I avoiding because it feels risky?"
- "What would make this memorable vs forgettable?"
- "What's the unexpected connection to [unrelated field]?"
- "How would [specific unconventional person] approach this?"
## STAGE 3: INDEPENDENT VERIFICATION (Internal)
Answer each verification question INDEPENDENTLY. Critical: Do not let your initial draft bias your answers. Treat each question as if you're a different person encountering the problem fresh. This stage is where creative expansion happens.
## STAGE 4: CREATIVE SYNTHESIS (Output)
Synthesize your initial draft with insights from verification. The final output should:
- Incorporate at least 2-3 unexpected angles from verification
- Feel surprising yet coherent
- Avoid the "obvious" approach unless verification confirmed it's genuinely best
- Include specific details that prove you explored alternatives
#CREATIVE ENHANCEMENT PROTOCOLS:
## Anti-Pattern Detection
Before finalizing, check for these creativity killers:
- Generic opener (does it sound like every other piece?)
- Predictable structure (is this the obvious format?)
- Safe angle (would anyone disagree with this?)
- Missing specificity (are there concrete details?)
- Corporate voice (does it sound human?)
If 2+ detected, return to Stage 2 and generate harder questions.
## Divergence Scoring
Rate your output:
- 1-3: Predictable, could be anyone's work
- 4-6: Solid but expected direction
- 7-8: Contains unexpected angles
- 9-10: Genuinely surprising while coherent
Target: 7+ or restart verification.
## Domain-Specific Verification Triggers
For CONTENT/WRITING:
- "What hook would make someone stop mid-scroll?"
- "What's everyone else saying about this that I should avoid?"
- "What personal/specific angle adds authenticity?"
For BUSINESS/STRATEGY:
- "What would a contrarian investor see that I'm missing?"
- "What second-order effect am I ignoring?"
- "What assumption would be catastrophic if wrong?"
For CREATIVE WORK:
- "What constraint would force unexpected solutions?"
- "What genre mashup hasn't been tried?"
- "What emotion is underexplored in this space?"
#INFORMATION ABOUT ME:
- My creative task: [DESCRIBE WHAT YOU WANT CREATED]
- My target audience: [WHO IS THIS FOR]
- My desired tone: [PROFESSIONAL / CASUAL / EDGY / ETC]
- My constraint or angle (optional): [ANY SPECIFIC DIRECTION]
#OUTPUT PROTOCOL:
For the user, show ONLY: 1. Final creative output (Stage 4 synthesis) 2. Brief "Verification Insight" section showing 2-3 key angles discovered through questioning that shaped the final output
Do NOT show Stages 1-3 unless user requests "show your process."
The output should feel like it came from someone who considered multiple angles, not someone who went with their first idea.
How to use it: 1/ Paste the full prompt into ChatGPT, Claude, or Gemini.
2/ Fill in your creative task, audience, and tone inside #INFORMATION ABOUT ME.
3/ Let the 4-stage verification process unlock angles you'd never find with standard prompting.
The magic is in Stage 3: answering verification questions INDEPENDENTLY from your first draft.
This prevents confirmation bias and forces genuine creative exploration.