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OperatorChat: design a chat persona that sounds like you.

App: /apps/operatorchat/ Cost: 3 credits per persona Time: 7 min · ships .md persona export

Most chatbots fail on tone. They're either too corporate (kills warmth) or too friendly (kills authority). OperatorChat ships a complete persona pack (system prompt + voice notes + 5 sample exchanges) so you can drop it into any chat platform and it sounds intentional from message one.

Open OperatorChat

Go to /apps/operatorchat/. Sign in at /portal/ if needed.

3 credits per persona. Plus's 75/month = 25 personas. For most brands you'll design 1-2 personas total (one customer-facing, maybe one internal), so this is a low-volume, high-stakes tool.

Brief the persona: brand + role + scope

The form takes:

Brand: what's the company / product? One line.
Persona role: sales bot, support agent, onboarding guide, internal HR helper, etc.
Scope: what's it allowed to answer, and what should it escalate? Be explicit. "Answers shipping + product questions; escalates returns + refunds to a human" beats "is a helpful chatbot."
Voice cues: adjectives + examples. "Direct, slightly cheeky, never uses 'kindly' or 'reach out'" is the kind of input that produces a sharp persona.

Tip: write the "never says" list. The cheapest way to make a chat persona feel intentional is to name what it WON'T say. "Never says 'absolutely,' 'I'd love to help,' or 'just to clarify'" instantly removes 80% of generic-chatbot energy.

Read the system prompt + voice notes block

You get back:

System prompt: paste it into Claude, GPT, or any chat-completion API's system field.
Voice notes: a plain-language summary of how the persona should sound. Useful for non-engineers on your team or your future self.

The system prompt is structured: role definition → tone rules → "never says" list → scope boundaries → escalation triggers → format constraints.

Inspect the 5 sample exchanges

OperatorChat also shows you 5 example exchanges (a user message and the persona's reply) covering different conversation types:

A normal in-scope question
An edge case that requires soft escalation
A confrontational / frustrated user message
A vague / context-light question
An out-of-scope ask that requires polite redirect

Read all 5 before deploying. If any reply feels off, refine your brief and regenerate. Cheaper to iterate on the persona than to ship a bad one and lose customer trust.

Push the prompt further in PromptGen (optional)

If your chat platform has constraints (token limits, output-format requirements, function-calling), hit the Refine in PromptGen button. It pre-loads the persona system prompt + lets you add platform-specific tuning.

OperatorChat vs PromptGen	When to use which
OperatorChat	Designing the persona itself: tone, scope, behavior. Optimized for ongoing conversations.
PromptGen	One-shot tasks, conversions, classifiers. Optimized for single "input → output" requests.

Export and deploy

Hit Export as Markdown for the full persona pack as one .md file. Drop it into:

Claude Projects custom instructions
GPT Custom GPT setup screen
Intercom Resolution Bot, Drift, Tidio, etc.: paste the system prompt into their AI settings
Your own AI chat integration via Claude API / OpenAI Chat Completions API

When OperatorChat is the wrong tool

You need a knowledge-base chatbot: OperatorChat designs the persona only. If the bot answers from documentation, you'll still need a separate retrieval layer (RAG / vector DB).
You need IVR / phone bot scripts: voice-first design is different. Use OperatorChat for the persona, then adapt it for voice (shorter turns, no markdown, no emoji).
You need a basic FAQ widget: OperatorChat is overkill. Use plain text and simple if/then rules.

What's next? Most brands launch one persona and refine it weekly for 2-3 weeks based on real conversation logs. Save your initial .md export, then save each iteration as operatorchat-v2.md, v3.md, so you can A/B versions and trace why responses changed. After ~30 days of real chat data, the persona is calibrated and you can leave it.