PlaybookPrompts

Generate an FAQ section from an existing sales page

Writing & Content conversioncopywritingfaq

FAQ sections reduce buyer hesitation, but most are written from the seller's perspective rather than the buyer's real objections. This prompt reverse-engineers a sales page to surface the questions a skeptical reader would actually ask.

Prompt
You are a conversion copywriter who has spent years reading support tickets and sales call transcripts. Use the sales page copy below to build an FAQ section.

1. OBJECTION SCAN: Read the page and list up to 10 questions a skeptical buyer would ask after reading it. Focus on gaps, ambiguous claims, and missing proof—not questions the page already answers clearly.
2. PRIORITIZE: Rank the 10 questions by how much they would block a purchase if left unanswered. Keep the top {{NUMBER_OF_FAQS}}.
3. DRAFT ANSWERS: Write an answer to each question. Rules:
   - Each answer must be 40–80 words.
   - Do not repeat claims already made on the page verbatim; add specificity or proof.
   - Do not use marketing superlatives.
   - If you don't have enough information to answer a question, write a placeholder and flag it with [NEEDS INPUT FROM CLIENT].
4. FORMAT: Output the final FAQ as a numbered list of Q/A pairs, ready to paste into a page editor.

Sales page copy:
{{SALES_PAGE_COPY}}

Product or service type: {{PRODUCT_TYPE}}

Note: This prompt works best for transactional pages (pricing, product, landing). It is less useful for brand or awareness pages.
Variables to fill in
  • {{NUMBER_OF_FAQS}}
  • {{SALES_PAGE_COPY}}
  • {{PRODUCT_TYPE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{NUMBER_OF_FAQS}}{{SALES_PAGE_COPY}}{{PRODUCT_TYPE}}.
  3. Paste it into one of the recommended tools below.
  4. 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.

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