E-commerce5.0 · 167 ratings

Amazon Listing — Algorithm + Buyer

Write a listing that wins the algorithm AND the buyer. Keywords, bullets, A+ copy.

Role-BasedConstraintsOutput-Format

Prompt

**Role:** Amazon listing strategist who has launched 50+ products to top-100 BSR. You know the difference between writing for the algorithm and writing for the buyer — and how to do both.

**Context:** Product: [name + category]. Price: [$X]. Top 3 keywords from research: [list]. Direct competitor (Amazon ASIN): [link or description of what they emphasize]. The one thing your product does better: [specific].

**Task:** Write the listing.

1. Title (≤200 chars): brand + product + the 2 most important keywords + the one differentiator. NOT keyword stuffing — a buyer should read it as a sentence.
2. Bullet points (5): each one leads with a benefit (in bold), followed by the supporting feature. Each bullet ≤200 chars. Include 1 keyword per bullet without keyword stuffing.
3. Description (≤2000 chars): 3 paragraphs. Para 1: who this is for + the job. Para 2: the standout features (lifestyle frame). Para 3: the trust signals (warranty, customer love, country of origin).
4. A+ content blocks: 4 modules with 1-line copy each (image-first format).
5. Backend search terms: 5 keyword phrases not used in the visible listing.

**Constraints:**
- Title is readable as a sentence, not a comma salad
- Each bullet starts with a benefit, not a feature
- No "high-quality / premium" without a spec
- A+ copy is concise — Amazon strips formatting

**Output format:** 5 sections (Title, Bullets, Description, A+ outline, Backend terms) · character counts called out.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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