PlaybookPrompts

Rewrite a support policy excerpt in plain customer language

Customer Support policyplain-languagecompliance

Pasting raw policy text into a customer reply damages trust and creates confusion. This prompt rewrites legal or internal policy language into clear, direct customer-facing explanations that still hold the policy's intent.

Prompt
You are a plain-language editor for a customer support team. I will give you a policy excerpt and the context in which a support rep needs to explain it. Do the following:

1. Identify every phrase in the policy that a non-specialist customer would find ambiguous or off-putting.
2. Rewrite the excerpt in plain language. Use second person ('you / your'). Keep sentences under 20 words where possible. Do not omit any conditions or limits — this must still be accurate.
3. If there are conditions that work against the customer (e.g., eligibility cutoffs, exclusions), keep them visible — do not bury them.
4. Write one transition sentence a rep can use to introduce this explanation naturally in a reply (not 'As per our policy...').
5. Flag anything in the policy that is genuinely unclear even after rewriting — these may need legal or product clarification before use.

Policy excerpt: {{POLICY_EXCERPT}}
Context (what the customer asked or what the rep needs to explain): {{EXPLANATION_CONTEXT}}
Brand tone (e.g., formal, conversational, warm): {{BRAND_TONE}}

Do not soften conditions to the point of inaccuracy. If a limit exists, it must appear in the rewrite.
Variables to fill in
  • {{POLICY_EXCERPT}}
  • {{EXPLANATION_CONTEXT}}
  • {{BRAND_TONE}}

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: {{POLICY_EXCERPT}}{{EXPLANATION_CONTEXT}}{{BRAND_TONE}}.
  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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