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.
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 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.
Outside of ChatGPT, GPT chatbots are customized to meet your specific needs, whether it’s an AI study buddy, a customer service chatbot, or a pocket-sized comedian.
These kinds of GPT chatbots can exist on a webpage – like ChatGPT – or they can be deployed to other platforms or channels.
You can customize a GPT chatbot and then integrate it to your WhatsApp or Facebook Messenger accounts. You can connect it to platforms like Zendesk or Salesforce, so that it helps you accomplish daily tasks, or a specific website, so it can distribute certain information.
Why should I use a GPT chatbot?
Most chatbots these days are built with existing large language models (LLMs) like GPT. If you have any kind of digital conversational task, you should consider using a GPT chatbot.
There are plenty of models you can use to power an AI chatbot – in fact, here’s a list of our favorite AI chatbot models – but GPT is the most common method.
Why? Unlike a lot of its competitors, you can easily connect a GPT chatbot to external platforms or messaging services, particularly if you use a third-party platform with built-in integrations.
Chatbot platforms make building your own GPT chatbot accessible and, in many cases, free. Even if you’re new to chatbots, the barrier to entry when building your own is low.
What can I use a GPT chatbot for?
In short, you can use a GPT chatbot for any conversational AI task.
If you use a chatbot platform to build your GPT chatbot, you can even make an AI agent that takes decisions and makes tangible changes in your workflows – like booking a meeting or providing up-to-date analytics on your users.
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.
A few examples of uses cases for a custom GPT chatbot include:
- A customer service chatbot available 24/7
- An HR bot that handles time off requests, scheduling, and information about policies
- A real estate chatbot that handles documentation, provides personalized recommendations, and schedules viewings
- A hotel chatbot that can book rooms, upsell services, and recommend activities
- A healthcare chatbot that tracks your symptoms
- An AI study buddy that tests you on the right flashcards, and remembers the best way to show you a math problem
GPT chatbots are especially useful for companies and enterprises that need safeguarded information, but they’re also an advanced way for individuals to incorporate AI efficiencies into their daily lives.
How do GPT chatbots work?
GPT chatbots use machine learning and natural language processing (NLP) to mimic human conversations with a user.
The most notable difference between GPT chatbots and old chatbots is that a GPT is trained on large datasets of text – and images, videos, and other media – that allow it to emulate natural language patterns.
The extensive training on a large dataset means that GPT chatbots are leagues ahead of the chatbots we all used a few years ago.
The meteoric rise in popularity of ChatGPT reflects its advanced natural language capabilities and its extensive dataset – combined, they make the most helpful digital text platform we’ve ever seen. And now chatbot platforms allow any company or individual to use the GPT engine to customize their own GPT chatbot.
How to Build a GPT Chatbot in 5 Steps
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.
Training 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.
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.
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.
Custom-trained vs. Ad hoc-trained
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.
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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.
Do I need to fine-tune my chatbot?
Unless you’re a major enterprise, you probably don’t need to fine-tune your chatbot. Methods and features like advanced prompting and RAG allow you to sufficiently personalize a chatbot.
What is a GPT chatbot?
A Generative Pre-trained Transformer (GPT) chatbot is a conversational software that uses a GPT model to power its interactions with users.
What is RAG?
Retrieval-augmented generation (RAG) is a technique used in generative AI that combines retrieval-based methods with AI generation.
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