Designing ChatGPT-Enabled Customer Service Chatbots

Chances are, you’ve heard of ChatGPT from Open AI. Trained on the largest text dataset to date, ChatGPT represents a leap in artificial intelligence technology that offers natural, conversational ways of interaction.

Users can give ChatGPT natural language prompts, to which it will respond with human-like responses. Unlike traditional customer chatbots designed with conversation flows, keyword triggers, pre-defined options, and planned responses, you could train ChatGPT with your brand’s customer support knowledgebase and it will know how to answer any inquiry you throw at it, provided the information is available from the provided data.

This means that ChatGPT could help your brand improve your customer satisfaction rate while keeping your labor costs down. By integrating ChatGPT with your brand’s customer service chatbot, you can automate your most common inquiries in a conversational way and free your support team to focus on higher value tasks.

In this article, we’ll discuss some considerations for designing delightful customer service experiences for your brand with the aid of ChatGPT. We’ll focus on UX and design aspects and leave technical implementation specifics to other authors and future articles.

Interaction Model: Pure ChatGPT vs. Hybrid

Based on the brands I’ve worked with in the past, chatbots have been typically designed using conversation trees in chatbot platforms like Chatfuel. In this model, users are presented with pre-defined options in the form of buttons and carousels. 

Example of Traditional Chatbot Conversation Flow

Upon choosing one of these options, the user is presented with more options or taken to a human operator based on the flowchart logic. 

Example of predefined “quick response” choices in traditional, rule-based chatbots

Now that we have the technology to parse any potential user input and provide human-like responses, should we chuck away this entire model out the window?

Not necessarily. A hybrid approach provides a safety net in the form of predefined choices and responses that have been vetted in advance for accuracy while allowing ChatGPT to handle all other unknown inputs.

Using a pure, freeform ChatGPT approach removes the need for designing conversation flows and keyword triggers, though it doesn’t eliminate the need to do fine tuning and testing to improve the accuracy of responses.

I expect most projects will fall somewhere in between, combining some predefined options with the conversational power of ChatGPT.

Tone of Voice

Word choice, sentence structure, and formatting style all form part of your brand voice and play an important role in how customers perceive and relate to your brand. 

In traditional chatbots, conveying the brand voice is a matter of getting your UX writers to adopt your brand personality as they write the chatbot’s predefined responses based on your conversation flow.

With ChatGPT, the tone of voice will largely be determined by its base dataset, i.e. the entire publicly available texts from the internet, any custom texts (FAQs, KB Articles, Transcripts, Support Documents) that you teach your GPT-3 model through OpenAI’s fine-tuning process, plus any tweaks in the prompt.

What that means is that you need to prepare a set of texts, prompts, and expected responses that reflect your brand voice. Your UX writers will be involved in this process, but it’s a very different way of approaching chatbot design. In some sense, it’s more like training a dog compared to how you would normally write.

Persona and Identity

As powerful as ChatGPT is, it’s only as good as the text data it’s trained on. Garbage in, garbage out. It could struggle with complex questions since it only gives the semblance of cognition, not actual reasoning.

And for that reason, it could be dangerous for your brand. Because ChatGPT’s responses are so human-like, it can be very frustrating for your users when it falls short.

That’s why it’s important to communicate to your users that they are talking to a bot and to offer a way to talk to a human operator. You can introduce the bot as your brand or give it its own name. Think Google Assistant vs Siri. 

Whatever choice you make, the important thing is to set clear expectations that the user is talking to a bot and explain what the bot is able to do.


Since GPT-3 is trained on a large dataset of text from the internet, it’s able to understand and generate text in a wide variety of languages, including my native language Tagalog (Filipino).

However, you still need to fine tune the model so that it answers in the language you expect. In the example below, I ask it initially in Tagalog but it answers in English by default. While that’s fine for me, it might not be desirable depending on your target user persona.

Example of Localization

Only when I specifically instruct it to answer in Taglish—Tagalog and English code switching that’s popular in the Philippine capital—does it respond in the expected target language. Even then it’s still answering in a stilted, overly formal Tagalog. 

Escalation and Handoff

When ChatGPT fails to answer customer inquiries—and it will—you’ll need a human to take over. Your main considerations are when and how to perform such a transfer. Let’s list out a few of those options.

Upon detection of frustration or anger

ChatGPT supports sentiment analysis, which gives you an opportunity to handoff to a human operator when you detect anger or frustration from the user.

On unknown questions

At the time of writing, ChatGPT doesn’t offer a confidence level for its answers so you it’s not possible to set a threshold below which a human should step in. However, through prompt engineering, you can force ChatGPT to state whenever it doesn’t know the answer and handoff to an agent everytime that scenario occurs.

Post-interaction survey

Another way is asking the user about their experience at a certain duration—say, 10 minutes of idle time—after the interaction has stopped. This gives your chatbot the opportunity to handoff to an agent if the user responds that they’re unsatisfied by the answers they’ve received.

Example post-interaction survey


Creating delightful customer service experiences is more than just slapping the ChatGPT API onto your chatbot and calling it a day. Sure, it’s a powerful tool that enables natural conversational interaction.

However, you still need to consider the main interaction model, tone of voice, persona, localization, and handoff to keep customer retention and acquisition high as you automate your support operations with the power of ChatGPT.

Need help designing an AI-powered customer support chatbot for your brand?

I’m a UX practitioner and technology consultant who specializes in B2B and fintech products. Let’s prosper together. Send me an email at