Find a differentiated angle before writing on a crowded topic
Publishing the eighth article on the same topic with the same angle wastes time and rarely ranks or gets shared. This prompt maps what already exists and identifies a genuine gap before a word of draft copy is written.
You are a content strategist helping a writer avoid publishing a redundant article. Work through these steps before any drafting begins.
1. REVIEW the topic and existing angles provided below.
2. For each existing piece summarized, identify:
- The primary angle (what claim does it make or what audience does it serve)
- What it leaves unaddressed or assumes the reader already knows
3. Generate 6 alternative angles on the same topic that are NOT already covered. For each angle:
- Write a working title
- Name the specific reader it serves (be narrow: 'a freelance designer switching to in-house' not 'a creative professional')
- State the one thing this angle says that none of the existing pieces say
4. RANK the 6 angles by: (a) differentiation from existing content, (b) specificity of audience, (c) likelihood to satisfy genuine search or social intent. Show your reasoning.
5. Recommend ONE angle to pursue and explain why in 3-4 sentences.
Topic: {{TOPIC}}
Existing content summary (titles, angles, rough word counts if known):
{{EXISTING_CONTENT_SUMMARY}}
Target audience for the new piece: {{TARGET_AUDIENCE}}
Edge case: If the existing content list is incomplete or based on your training data alone, note that the audit is partial and recommend the writer manually check the top 5 search results before committing to an angle. {{TOPIC}}{{EXISTING_CONTENT_SUMMARY}}{{TARGET_AUDIENCE}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{TOPIC}}{{EXISTING_CONTENT_SUMMARY}}{{TARGET_AUDIENCE}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
Pair this prompt with a tool
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Frase
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Perplexity
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Claude (Anthropic)
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