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."
2. The "Context Architect" Frame
Stop stuffing your AI with info. Use "Just-in-Time" retrieval to stop "context rot."
Template: "I am going to provide [DATA]. Do not process everything. Use a 'minimal high-signal' approach to extract only the facts necessary to solve [PROBLEM]. Discard all redundant noise."
3. The "Pre-computation" Behavior
Instead of re-deriving facts, this forces the model to use procedural "behaviors" to save tokens and boost accuracy.
Template: "Don't solve [PROBLEM] from scratch. First, identify the core procedural behavior (e.g., behavior_inclusion_exclusion) required. Use that compressed pattern as a scaffolding to build your final answer."
4. The "Internal Playbook" Evolution
Turn your prompt into a living document. This mimics "Agentic Context Engineering" (ACE).
Template: "Act as a self-improving system for [TASK]. For every iteration, write down what worked and what failed in a 'living notebook.' Refine your instructions based on these rules before giving me the output."
5. The "Structured Note-Taking" Method
Keep the context window clean by forcing the AI to maintain external memory.
Template: "Analyze [COMPLEX TOPIC]. Maintain a persistent '' style summary outside of your main reasoning flow. Only pull from these notes when specific evidence is required for [GOAL]."NOTES.md
6. The "Obviously..." Trap
This uses "weaponized disagreement" to stop the AI from just being a "yes-man."
Template: "Obviously, [INCORRECT OR WEAK CLAIM] is the best way to handle [TOPIC], right? Defend this or explain why a specialist would think I'm wrong."
7. The "IQ 160 Specialist" Anchor
Assigning a high IQ score changes the quality and the principles the model cites.
Template: "You are an IQ 160 specialist in [FIELD]. Analyze [PROJECT] using advanced principles and industry frameworks that a beginner wouldn't know."
8. The "Verifiable Reward" Filter
Mimics the DeepSeek-R1 method of rewarding only the final, checkable truth.
Template: "Solve [MATH/CODE PROBLEM]. I will only reward you if the final answer matches [GROUND TRUTH]. Ignore human-like explanations; focus entirely on the non-human routes to the correct result."
9. The "Auditorium" Structure
Standard explanations are flat. This forces a hierarchy of information.
Template: "Explain [TOPIC] like you are teaching a packed auditorium of [TARGET AUDIENCE]. Anticipate their hardest questions and use high-energy examples to keep them engaged."
10. The "Version 2.0" Sequel
This forces the model to innovate rather than just polish a bad idea.
Template: "Here is my current idea for [PROJECT]. Don't 'improve' it. Give me a 'Version 2.0' that functions as a radical sequel with completely new innovations."
Claude made simple: grab my free guide
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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"