Competitor Teardown Prompt
Deep-dive one competitor from public artifacts.
/ quick answer
Run against a target competitor URL + LinkedIn + reviews. Deep-dive one competitor from public artifacts.
Teardown of {{competitor}}.
Sections:
- Positioning (1 sentence, in their own words if possible).
- Pricing (with tiers if visible).
- Ideal customer (inferred from casebooks + reviews).
- 3 differentiators (with proof).
- 3 weaknesses (from reviews + missing features).
- One angle to compete on.
Sources: {{sources}}[Structured teardown with proof-linked claims]
/ frequently asked
What does the Competitor Teardown Prompt prompt do?
Run against a target competitor URL + LinkedIn + reviews.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
[Structured teardown with proof-linked claims].
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Deep Research (AI)
Long-running AI research task that produces a cited multi-page report.
- →Research Automation
Research automation turns a question into a sourced, structured answer using search, retrieval, extraction and synthesis agents.
Related workflows
Turn this into a repeatable process.
- →Automated Competitor Research
From a product description to a structured competitor matrix in under 10 minutes.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
- →AI Market Research Report in One Afternoon
From blank page to sourced, structured 20-page market report in ~4 hours.
- →Turn Deep Research Into a Weekly Executive Brief
Use an AI Deep Research agent every Monday to produce a cited market brief in 20 minutes.
Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
- →Research Automation Stack
Search, fetch, extract and synthesise sourced briefs on a schedule.
- →AI Crypto Research Stack
A read-only research stack combining an AI assistant, web search, market data and on-chain analytics to screen assets quickly.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
Related prompts
Reusable prompts for this job.
- →Deep Research Prompt Template
Reusable Deep Research prompt that produces cited, structured reports every time.
- →Sourced Research Brief Prompt
Produces a structured brief where every claim carries a citation.
- →Market Research Synthesizer
Compress 10-20 sources into a 1-page decision-grade brief.
- →Buyer Persona Builder Prompt
Generate a detailed persona from a product description and target market.
Related use cases
How people apply it, and what came out.
- →Tech Creator Replaces a Research Assistant With a Workflow
A YouTuber cut their research time per video from 8 hours to 90 minutes.
- →Research A Token With AI
A structured AI research pass cut token screening from three hours to 35 minutes and produced documented passes instead of impulse entries.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
Comparisons & alternatives
Pick between the options.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.