Free prompt optimiser

Customer Support Prompt Optimiser

Build AI customer-support replies and macros from a prompt that states the issue, tone, policy constraints and required response format.

Try it with this example →

Runs with Local Rules, free, in your browser — no sign-up needed.

What this optimiser checks

  • Whether the customer's situation and what they've asked for is described
  • Whether the desired outcome (refund, replacement, apology, timeline) is stated
  • Whether tone and brand-voice constraints are given
  • Whether policy boundaries (what you can and can't offer) are included
  • Whether the output format (email, chat message, macro with variables) is specified

Example prompt

Before
Write a reply to a customer who says their order hasn't arrived.

"Write a reply to a customer who says their order hasn't arrived" doesn't say what your actual policy is for late orders, what tone your brand uses, or what outcome you're offering — so the AI-generated reply may promise something support isn't authorised to give.

Local Rules rewrites the prompt to require the policy constraints and desired outcome up front, and to state the format you need — a one-off reply, or a reusable macro with placeholders like [order number] and [customer name]. That structure reduces the risk of AI-drafted replies that overpromise or contradict company policy.

Frequently asked questions

Can this guarantee the reply follows our support policy?
No — it structures the prompt so policy constraints you provide are more likely to be respected, but you still need to review AI-generated replies before sending them, especially anything involving refunds or compensation.
Can I generate a reusable macro instead of a one-off reply?
Yes — state that you want a reusable template with placeholders in your prompt, and the Output format section of the rewrite will carry that requirement through.
Does it send customer data anywhere?
Local Rules processes everything in your browser with no network request. Avoid pasting real customer personal data regardless, and use placeholders like [customer name] instead.
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