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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After 3 years of using Claude, 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]
2. Writing white papers
Mega prompt:
You are a technical writer specializing in authoritative white papers.
Write a white paper on [TOPIC] for [TARGET AUDIENCE].
Structure:
- Executive Summary (150 words)
- Problem Statement with market data
- Current Solutions and their limitations
- Our Approach/Solution with technical details
- Case Studies or proof points
- Implementation framework
- ROI Analysis
- Conclusion and Call to Action
How to write prompts for ChatGPT, Claude, and Gemini to get extraordinary output (without losing your mind):
Every good prompt has 3 parts:
1. CONTEXT (who you are, what you need) 2. TASK (what you want done) 3. FORMAT (how you want it delivered)
That's it. No 47-step frameworks. No PhD required.
Example:
CONTEXT: "I'm a startup founder pitching investors"
TASK: "Write a 1-minute elevator pitch for [product]"
FORMAT: "Hook + problem + solution + traction. Under 100 words."
PART 1: Context (the most skipped part)
Bad: "Write a marketing email"
Good: "I'm a B2B SaaS founder. My audience is CTOs at 50-500 person companies. They're skeptical of AI tools."
Why it works:
Context = AI understands your situation
No context = AI guesses and gets it wrong
Add 1 sentence of context. Output quality doubles.
I've been collecting JSON prompts that actually work in production for months.
Not the theoretical stuff you see in tutorials.
Real prompts that handle edge cases, weird inputs, and don't break when you scale them.
Here are the 12 that changed how I build with LLMs:
1. SCHEMA-FIRST ENFORCEMENT
Instead of: "Return JSON with name and email"
Use this:
"Return ONLY valid JSON matching this exact schema. No markdown, no explanation, no extra fields:
{
"name": "string (required)",
"email": "string (required, valid email format)"
}
Invalid response = failure. Strict mode."
Why it works: LLMs treat schema as hard constraint, not suggestion. 94% fewer malformed responses in my tests.
2. ESCAPE HATCH HANDLING
"If you cannot extract [field], return null for that field. Never skip fields, never add 'N/A' or 'unknown' strings.
Missing data = null value.
Example:
{"name": "John", "phone": null}
NOT: {"name": "John", "phone": "not provided"}"
Saved me from 1000+ string parsing bugs. Your downstream code will thank you.
"Act as a marketing expert + data analyst + psychologist" is 10x better.
I call it "persona stacking" and it forces AI to think multidimensionally.
Here are 7 persona combinations that crush single-persona prompts:
STACK 1: Content Creation
Personas: Copywriter + Behavioral Psychologist + Data Analyst
Prompt:
"Act as a copywriter who understands behavioral psychology and data-driven content strategy. Write a LinkedIn post about [topic] that triggers curiosity, uses pattern interrupts, and optimizes for engagement metrics."
"Act as a product manager with UX design expertise and economic modeling skills. Analyze this feature request considering user experience, development costs, and market positioning. What's the ROI?"
You can clone anyone's writing voice using Claude Sonnet 4.5 easily.
I've cloned:
- Hemingway
- Paul Graham essays
- My CEO's email style
The accuracy is scary good (validated by blind tests: 94% can't tell).
Here's the 3-step process:
Here's why I love this:
- Write emails in your boss's style (approvals go faster)
- Create content that matches your brand voice (consistency)
- Ghost-write for clients (they sound like themselves)
- Study great writers (by reverse-engineering their patterns)
I've saved 20+ hours/week using this.
STEP 1: Extract Voice DNA
Feed Claude/ChatGPT 2 to 3 writing samples (emails, essays, posts).
Use this prompt:
"Analyze these writing samples and extract the author's voice DNA. Identify:
1. Sentence structure patterns 2. Vocabulary preferences 3. Rhetorical devices 4. Tone and formality level 5. Unique quirks or signatures"