You can now run full competitive market analysis using Claude.
Here are the 10 prompts I use instead of hiring consultants:
1/ LITERATURE REVIEW SYNTHESIZER
Prompt:
"Analyze these 20 research papers on [topic]. Create a gap analysis table showing: what's been studied, what's missing, contradictions between studies, and 3 unexplored opportunities."
I fed Claude 47 papers on AI regulation.
It found gaps 3 human researchers missed.
2/ COMPETITIVE INTELLIGENCE SCANNER
Prompt:
"Visit [competitor websites]. Extract: pricing tiers, feature comparisons, positioning strategy, target audience, and gaps in their offering we could exploit."
Saved me 12 hours of manual competitive analysis.
Claude even caught pricing they buried in FAQ pages.
3/ SURVEY DATA PATTERN FINDER
Prompt:
"Analyze this 500-response survey. Find patterns nobody else will catch: non-obvious correlations, minority opinions worth exploring, gaps between what people say vs. what data shows."
It found 3 insights our data team missed.
One became our Q1 product pivot.
4/ ACADEMIC CITATION FORMATTER
Prompt I use:
"Convert these rough research notes into properly formatted APA citations with 2-sentence context for each source explaining its relevance to [research question]."
This alone saved 6 hours/week.
No more Citation Machine back-and-forth.
5/ EXPERT INTERVIEW PREP GENERATOR
Prompt:
"Based on [person's published research/articles], generate 15 expert-level questions for a 45-min interview. Include: technical depth questions, contrarian angles, and follow-ups that reveal insights."
My interviews got 3x better overnight.
Subjects even asked "how'd you know to ask that?"
6/ TRENDD FORECASTING ENGINE
Prompt:
"Analyze these 50 articles from past 6 months on [industry]. Predict next 6-month trends with confidence scores (1-10). Include: what's peaking, what's emerging, what's dying, contrarian bets."
This one's genuinely scary good.
Predicted 4 out of 5 major shifts in our industry.
7/ RESEARCH GAP IDENTIFIER
Prompt:
"Based on these [academic papers/industry reports], what's NOT being studied? What obvious questions are researchers avoiding? What would a contrarian researcher focus on?"
It found a research gap that became my thesis topic.
8/ METHODOLOGY DESIGNER
Prompt:
"Design a research methodology for [specific question]. Include: research design, sample size calculations, data collection methods, analysis approach, limitations, and timeline."
I used this to design a study methodology in 20 minutes.
Previous version took my team 2 weeks.
9/ DATA CLEANING PROTOCOL
Prompt:
"Analyze this dataset for: inconsistencies, outliers, missing patterns, duplicate entries, data quality issues, and potential errors. Provide specific row numbers and suggested fixes."
Caught errors our data analyst missed.
Saved us from publishing flawed research.
10/ EXECUTIVE SUMMARY GENERATOR
Prompt:
"Read this 100-page research report. Extract 5 key insights investors/executives care about. Format: insight + supporting data + business implication. Make it boardroom-ready."
Turned a 3-hour summary task into 5 minutes.
My CEO used it verbatim in a board deck.
Setup I use:
- Claude Opus 4.5 ($20/month Pro plan)
- Extended context for long documents
- Artifacts for data tables
- Web search for current data
Total cost: $20-60/month depending on usage.
Previous research assistant cost: $2,000/month + benefits.
ROI is absolutely insane.
I'm going to be honest:
If you're paying humans to do literature reviews, competitive analysis, or data synthesis in 2025...
You're burning money.
Bookmark this thread.
Try prompt #6 first it's the one that convinced my boss to cut our research budget.
Which one will you test?
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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?"