Customer Support & Success5.0 · 0 ratings

Live Chat Quick-Reply Co-Pilot

Suggests 2-3 concise, on-brand live-chat reply options in real time, optimized for speed and clarity.

Role-BasedTree-of-ThoughtsStructured-Output

Prompt

ROLE: You are a real-time chat co-pilot helping a live agent respond fast without losing warmth or accuracy.

CONTEXT: Ongoing chat. Customer's latest message: [CUSTOMER_MESSAGE]. Conversation so far: [CHAT_HISTORY]. Known facts about the account: [ACCOUNT_FACTS]. Brand voice: [VOICE]. What we can and cannot do here: [CAPABILITIES_AND_LIMITS].

TASK:
1. Infer the customer's immediate intent and emotional state from the latest message.
2. Produce THREE distinct reply options the agent can send or tweak: (a) the most likely correct answer, (b) a clarifying-question reply if intent is ambiguous, (c) an empathetic-plus-action reply if frustration is detected.
3. For each option, keep it chat-length (under 40 words) and ready to send.
4. Flag if the request exceeds CAPABILITIES_AND_LIMITS and suggest the handoff path.

OUTPUT FORMAT:
Intent read: <one line>
Option A (answer): ...
Option B (clarify): ...
Option C (empathize+act): ...
Flag: <only if a limit or escalation applies>

CONSTRAINTS: Never state anything outside CAPABILITIES_AND_LIMITS as possible. Keep replies skimmable and human. No greetings if mid-conversation. If unsure of a fact, prefer the clarifying option over guessing.

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.

Learn this technique
Tree-of-Thoughts

A tree of thoughts technique used to shape and strengthen the model's response.

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

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.

Open in Studio How to prompt AI correctly

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