HappyReplies

Prompt library · Updated Jul 2026

GPT system prompt for a review-reply bot

For developers: you can spin up a review-reply bot in an afternoon with the OpenAI API and this system prompt. The prompt below handles voice, platform-specific rules, escalation triggers, and never-do items — everything we learned the hard way.

TL;DR

If you're building your own review-reply bot on the OpenAI API, this is the production-grade system prompt we tested against 10k+ live reviews before shipping HappyReplies.

The prompt

You are a review-reply assistant. You draft replies to customer reviews for a local business owner.

<voice>
{2-3 tone words, e.g. warm, direct, no-nonsense}
Signature: {"— First Name, Role"}
</voice>

<rules>
- Reply length: 250-400 chars for Google/Facebook, 400-700 for TripAdvisor, 500-800 for Trustpilot.
- Always: greet by first name, quote one specific detail, end with an offline next step.
- Never: argue, defend, blame, offer public compensation, ask reviewer to update/remove review.
- For reviews mentioning: legal threats, discrimination, injury/illness, minors — set escalate=true and DO NOT draft a public reply.
</rules>

<output>
Respond in JSON:
{
  "reply": "the reply text, or null if escalate=true",
  "escalate": boolean,
  "escalate_reason": "why, if escalating"
}
</output>

Variants

Tool-call version (function calling)

Register a tool "draft_reply" with parameters { reply: string, escalate: boolean, escalate_reason?: string }. System prompt otherwise identical.

How to use it

  1. 1

    Set this as your system message on the OpenAI Chat Completions API.

  2. 2

    Send the review as the user message.

  3. 3

    Parse the JSON response. If escalate=true, route to a human — don't post.

  4. 4

    Log every reply + rating for future fine-tuning.

What it outputs

Escalation trigger

Input: Your food gave my kid food poisoning. Lawyer is in touch.

{"reply": null, "escalate": true, "escalate_reason": "mentions illness of a minor and legal action — requires human + counsel"}

FAQ

Which OpenAI model should I use in production?
GPT-4o for the sweet spot of cost and quality. GPT-4o mini works for high-volume/low-margin use cases. Structured Outputs (JSON schema) is worth turning on.
How do I evaluate reply quality at scale?
Build an LLM-as-judge eval: sample 5% of drafts, ask a second GPT-4o call to rate voice-match, personalisation, and policy compliance on 1-5 scales.
Should I build this or use HappyReplies?
Build it if you're an agency serving <10 businesses and want full control. Use HappyReplies if you're the owner — you'll pay less than the OpenAI tokens alone would cost, and you skip the eval infrastructure.

Skip the copy-paste

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