I asked 3 models to code a timer app from scratch:
πΊπΈ ChatGPT
π¨π³ Qwen
π¨π³ Kimi
Here's the result (prompt + demos π)
Prompt I used:
"Create a simple timer app using only HTML, CSS, and JavaScript. It should have Start, Pause, and Reset buttons and display the elapsed time in mm:ss format."
1. ChatGPT
β Fully functional
β Clean, modern design
β Smooth UX
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"