What is a GPT chatbot and how do I build one?

Thanks to OpenAI’s open LLM, you can build your own GPT chatbot powered by the world’s latest AI technology. 

Large language models (LLMs) like GPT are advancing rapidly year over year. That not only means they’re more powerful, but that there are more accessible ways to build your own custom GPT chatbot.

We've helped over 750,000 people build and deploy their own LLM-based chatbots. So we understand a thing or two about how to use the GPT engine to customize your own chatbot.

In this article, I’ll walk you through:

• The basics of GPT chatbots
• The training behind the GPT model
• The steps to build your own GPT chatbot

What is a GPT chatbot?

A Generative Pre-trained Transformer (GPT) chatbot is a conversational agent that uses a GPT model to power its interact with users.

Typically, we think of ChatGPT when we talk about GPT chatbots. But OpenAI’s GPT engine can power many different types of chatbots – some built directly on OpenAI, and others built on chatbot platforms that use the GPT engine.

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Outside of ChatGPT, GPT chatbots are customized to meet your specific needs, whether it’s an AI study buddy, a customer service chatbot, a sales chatbot, a scheduling bot, or even an HR chatbot.

These kinds of GPT chatbots can exist on a webpage – like ChatGPT or a company customer support bot – or they can be deployed to other platforms or channels (like a WhatsApp chatbot). 

You can deploy a customized GPT channel on a channel like Telegram, or even connect it to platforms like Zendesk or Salesforce. It can use data from your business to help inform customers or help employees make decisions.

Why should I build a chatbot with GPT or another LLM?

Most chatbots these days are built with existing large language models (LLMs) like GPT.

Why? They're powerful, they get more affordable with every new release, and they're far, far too complex a technology for most companies to build.

So if you have any kind of digital conversational task, you'll probably end up using a GPT chatbot. 

GPT bots are powerful

A study from the City University of Hong Kong highlights the power of customized GPT chatbots, explaining that by "leveraging customized data, the chatbot can provide users more targeted and tailored information, enhancing the overall user experience."

This ability to deliver context-aware, personalized responses makes GPT chatbots an invaluable tool - when else in history have we been able to use advanced AI technology to help us book a flight or plan a meal?

GPT bots get more affordable with every release

Most of our users (like . . . a full 95% of them) opt for GPT models over any other company's LLMs. Why? At least at the time of this publishing, the 4o model is the best bang for your buck.

So the OpenAI models are the most affordable for a reliable AI experience right now. But in 6 months time, who's to say what model might be inthe lead?

What can I use a GPT chatbot for?

In short, you can use a GPT chatbot for any conversational AI task.

The most common use cases are customer service, sales, marketing, booking bots, and internal employee chatbots (like HR or IT bots).

But if you're using a flexible chatbot platform, you can build anything you can think of. A pocket-sized comedian. A personal planner. Education chatbots or healthcare bots. Anything.

We have customers who have built real estate chatbots, restaurant chatbots, and even hotel chatbots that books rooms and coordinates staff.

You can get daily updates on stocks from a crypto agent. You can build an AI study buddy. You can even build a GPT chatbot for WhatsApp that interacts with your users over a messaging channel. Really, the sky is the limit.

How do GPT chatbots work?

Input and Preprocessing

A user types or speaks a message to the chatbot. The text gets cleaned up and structured — sometimes tagged with context like the conversation history or metadata. This preprocessing helps the model understand the request in the right frame.

Language Model Processing

The chatbot sends the input into the GPT engine (for example, GPT-4o).

GPT predicts the most likely next word, one after another, until it forms a complete, human-sounding response. It relies on the patterns it has learned from vast training data, so you don't need to train it. Thanks natural language processing!

However, if you want to train a chatbot on custom information (like customer logs), then a strong chatbot building platform will allow you to add training materials of your own.

Conversation Memory

To keep track of ongoing conversations, chatbots use context windows or memory features.

The model doesn’t remember past chats on its own, so developers feed it the relevant history each time. This allows it to respond as if it “remembers” what was said earlier.

If this is an important part of the chatbot you're building, be sure to ask your provider about memory capabilities — many platforms don't offer it! Platforms like Botpress or frameworks like LangChain offer memory capabilities, though.

Business Logic and Integrations

Most GPT chatbots aren’t just “raw GPT.” They’re connected to tools, databases, or APIs.

This means if you ask for your order status, the chatbot uses GPT to understand your request, then calls the business’s order system, and finally generates a natural response with the retrieved data.

Post-processing and Guardrails

Before the message reaches the user, developers can add rules, filters, or formatting. This is where things like tone adjustments, content safety checks, or company-specific policies come in. These guardrails make sure the chatbot answers in line with brand and compliance requirements.

Output to the User

Finally, the chatbot delivers the generated response through the chosen channel—like a website widget, messaging app, or voice assistant. The cycle then repeats with the next user message.

How to Build a GPT Chatbot in 5 Steps

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If you’re looking to build your own GPT chatbot, breathe a sigh of relief. The hardest part has already been done by the pros. And now the general public is able to customize the powerful GPT engine for their own uses.

There are two main ways to build your own GPT chatbot: building a custom GPT on OpenAI, or building a custom GPT chatbot on a third-party platform. Don’t worry, there are plenty of free options. 

Step 1: Define your scope

Decide what your chatbot will be used for. Maybe it’s a bot for personal use that will track your grocery spending and help with meal planning. Or maybe your company is looking for an AI agent to orchestrate your customer service and information management. 

Your scope should include who you want to build your chatbot for – yourself, your customers, your employees, your users, anyone on the internet – and what capabilities it will need to have in order to accomplish its goals. 

For example, if you want a chatbot for real estate or a hotel, you should find a platform that offers a built-in integration with Facebook Messenger, Telegram, or WhatsApp, so you can directly communicate with your audience.

Once you’ve defined your audience and your chatbot’s needed capabilities, you can find a platform that supports them.

Step 2: Choose your platform

No matter what type of chatbot you want to build, there’s a platform that has everything you need. 

For example, if you want to build a bot without writing a line of code, there are no-code options available. 

If you want a heavily customized chatbot that connects to your bespoke systems and workflows, you’ll want to find a highly extendable platform that allows you to build endless possibilities. 

If you want to build a WhatsApp GPT bot or a Slack chatbot, you’ll need to find a platform with a built-in integration. 

If you need inspiration, check out the list of our top 9 chatbot platforms.

Step 3: Collect your data

If you want to conduct advanced prompting or fine-tuning, you’ll need to collect the dataset that will inform your chatbot. 

For example, if you want to relieve your customer support team by building a bot that mimics their techniques, you can collect transcripts of successful customer service calls. 

Step 4: Customize and integrate

The most exciting part? Actually building your GPT chatbot. 

Your chatbot platform will allow you to customize the actions your chatbot takes, the tone or personality it emulates, and individual conversation flows. 

You can even prompt your chatbot to complete a certain task, and it can autonomously accomplish it.

You’ll also need to integrate your chatbot with any necessary sources of information. For example, if you want it to explain your products, your GPT chatbot needs to be connected to your website and product catalog.

Step 5: Deploy and test

Where do you want your GPT chatbot to be accessed?

You’ll likely want to deploy your bot to a website, but it may be useful to deploy it to other channels, too. Depending on its purpose, you may want to set it up on your customers’ most popular messaging channel, or on the platforms most used by your employees. 

Once your chatbot is built, you or your team will need to test out different situations and iterate on your chatbot.

How can I train a GPT model?

If you’re interested in building your own GPT chatbot, it’s useful to understand how the GPT model was created. 

A GPT model is born from pre-training, and can be further specialized with fine-tuning. However, you can also build a customized GPT chatbot that doesn’t involve fine-tuning, which is an intensive process that can quickly become expensive.

Pre-training

Pre-training is a time- and resource-intensive process that – for the time being – can only be completed by well-funded enterprises. If you’re building your own GPT chatbot, you won’t be pre-training it. 

Pre-training occurs when a development team trains the model to be able to accurately predict the next word in a human-sounding sentence. After the model is trained on a large amount of text, it can more accurately predict which words should follow which in a sentence.

A team starts by collecting a massive dataset. The model is then trained to break down the data by dividing text into words or subwords, known as tokens. 

This is where the ‘T’ in GPT comes in: this text processing and breakdown is done by a neural network architecture called a transformer. 

By the end of the pre-training phase, the model understands language broadly, but isn’t specialized in any particular domain. 

Fine-tuning 

If you’re an enterprise with a huge dataset at your fingertips, fine-tuning might be on the table.

Fine-tuning is training a model on a specific dataset, in order for it to become a specialist in a specific function. 

You might train it on:

• Medical texts, so it can better diagnose complex conditions
• Legal texts, so it can write higher-quality legal briefings in a particular jurisdiction
• Customer service scripts, so it knows what types of problems your customers tend to have

After fine-tuning, your GPT chatbot is powered by the language capabilities it gained in pre-training, but also specialized in your custom use case.

But fine-tuning isn’t the right process for a lot of GPT chatbot projects. You don’t need fine-tuning if you’re trying to customize a chatbot. 

In fact, you can only fine-tune a GPT chatbot if you have a very large dataset of relevant information (like the customer service call transcripts for a large enterprise). If your dataset isn’t large enough, it isn’t worth the time or cost to fine-tune. 

Luckily, advanced prompting and RAG (retrieval-augmented generation) are almost always sufficient for customizing a GPT chatbot – even if you’re deploying it to thousands of customers.

What are the alternatives to training a GPT chatbot? 

If the training process seems daunting, there’s good news. You probably don’t need to.

Fine-tuning a GPT chatbot is useful for specific needs of major enterprises – and available for our Enterprise customers – but most companies and chatbot builders can achieve their desired results without the expensive fine-tuning process. 

If you’re looking to train your own GPT chatbot to:

• Speak in your brand voice
• Balance being empathetic and helpful
• Correctly detect a specific problem faced by your customers 
• Disseminate specific brand information

Then you don’t need to go to the trouble of fine-tuning your chatbot. Chatbot builder platforms will allow you to complete advanced prompting that tailors your bot to your exact needs. 

Advanced prompting

The best chatbot platforms will provide opportunities for advanced prompting when you’re building your GPT chatbot.

Different types of advanced prompting will allow you to instruct your bot on how to respond to certain scenarios. If you want it to promote one kind of product more than another, or you want it to disseminate accurate information about Roman history, you can prompt your bot in the building phase. 

Some builders find it useful to employ AI prompt chaining or chain of thought prompting, two strategies that improve the reasoning and explainability of a model.

RAG

Retrieval-augmented generation (RAG) is a type of AI generation that instructs your chatbot to pull information from a specific source – usually your internal tables, documents, or websites – and generate a response based on that information.

If you’re worried about building a GPT chatbot that recommends the competitor or gives out false deals, RAG is a way to confine your chatbot’s answers to a certain dataset. Most companies that use a GPT chatbot use RAG to safeguard its output.

“AI hallucination is very solvable,” said Nvidia CEO Jensen Huang, noting that RAG transforms AI into “a research assistant summarizing for you.”

So if you don’t have the time or resources to fine-tune a chatbot, don’t stress. There’s no need to fine-tune a chatbot in order to build a customized, on-brand GPT chatbot.

What's the difference between custom-trained and ad hoc-trained?

In short: Custom-trained GPTs are tailored with business-specific data for higher accuracy, while ad hoc-trained GPTs rely on general datasets for broader but less specialized responses.

Custom-trained GPTs

Custom-trained GPTs are created by training them on specific datasets. 

These contain relevant customer inquiries and answers related to the particular business they're used for. With this approach, businesses can ensure their chatbot provides knowledgeable solutions tailored specifically to their organization's needs.

Ad hoc-trained GPTs

Ad hoc-trained GPTs use existing data sets designed for general usage. While they require less customization compared to those that are custom-trained, their accuracy may be slightly lower than that of their custom-trained counterparts. 

Nevertheless, when equipped with proper AI technology such as NLP, these bots become powerful tools capable of generating useful replies even in complex conversations.

Build a Custom GPT Chatbot

Combining the power of the GPT engine with the flexibility of a chatbot platform means you can use the latest AI technology for your organization’s custom use cases. 

Botpress provides a drag-and-drop studio that allows you to build custom GPT chatbots for any use case. We let you make AI work for you, no matter how you want to deploy it. 

We feature a robust education platform, Botpress Academy, as well as a detailed YouTube channel. Our Discord hosts over 20,000+ bot builders, so you can always get the support you need. 

Start building today. It’s free. 

Or contact our sales team to learn more. 

FAQ

Is GPT unique to OpenAI?

The name GPT is unique to OpenAI, although they were denied the copyright to it. But the method of creating a GPT can be done by anyone with enough resources. Usually when people say 'GPT bot', they're referring to an LLM-powered chatbot that uses a GPT model.

Should I fine-tune my chatbot?

Unless you’re a major enterprise, you probably don’t need to fine-tune your chatbot. Methods like advanced prompting and RAG are sufficient for most companies looking to build a bespoke chatbot.

How can I customize a GPT chatbot?

The easiest ways to customize a GPT bot are advanced prompting or using RAG (retrieval-augmented generation). These allow you to dictate how your bot behaves and where it draws its knowledge from. These forms of instruction are usually sufficient for companies to build a robust custom chatbot.

Is building a GPT chatbot hard?

It doesn't have to be difficult to build a GPT-powered chatbot, especially with the rise of low-code chatbot platforms. You can even build a GPT bot without any code by using drag-and-drop bot platforms like Botpress.