With the rapid advancements in AI technology, it's becoming more accessible for individuals to build their own GPT chatbots.
OpenAI's generative pre-trained transformer model – the engine behind ChatGPT – has become a resource for those looking to build their own AI agents and software.
Learning how to customize your own GPT agent allows you to harness the most powerful technologies of our time for your specific use cases. So let's get started.
What is a GPT model?
A GPT model (generative pre-trained transformer) is an advanced type of language model developed by OpenAI. It uses deep learning techniques to understand and generate human-like text.
GPT models are trained on vast amounts of text data to predict the next word in a sequence, allowing them to perform tasks like answering questions, writing content, and even coding.
These models are widely used in applications like AI chatbots, content generation, and translation.
GPT models have been used in the real world as the engines behind customer support chatbots, lead generation agents, and research tools across disciplines. These AI chatbots can be found everywhere online, from healthcare and e-commerce to hotels and real estate.
Who can train GPT models?
Training a GPT model is a labor- and resource-intensive task. Typically, you'll need to have a team with funding behind you - like a research institute, a well-funded company, or even a university - in order to have the necessary resources to train a GPT model.
However, it's far more accessible for individuals or companies to train their own GPT chatbots. By training a GPT chatbot instead of a model, you gain all the powerful capabilities of a GPT model, but can easily customize it to your own needs.
How are GPT models trained?
To train a GPT model on your own, you must be prepared – financially and otherwise – to use powerful hardware and invest a significant amount of time perfecting algorithms.
A GPT model is borne 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.
3 ways to customize LLMs
Whether or not it's a GPT engine, customizing an LLM comes with a wealth of benefits. It can keep your data private, reduce costs for specific tasks, and improve the quality of answers within your use case.
Botpress software engineer Patrick explains the ins and outs of customizing an LLM in this article. Here are his top suggestions for LLM customization:
1. Fine tuning
Fine-tuning involves training a model with specific examples to make it excel at a particular task, such as answering questions about your product.
While open-source models require engineering capacity for fine-tuning, closed-source models like GPT-4 or Claude can be fine-tuned via APIs, though this increases costs. Fine-tuning is especially useful for static knowledge but isn't ideal for real-time information updates.
2. RAG
Retrieval-augmented generation (RAG) refers to using external information, like a document of HR policies, to answer specific questions.
It's ideal for accessing real-time information, such as a chatbot checking a product catalog for stock, and avoids the need for fine-tuning models.
RAG is often easier and more cost-effective to maintain for knowledge-based chatbots, as you can query up-to-date data without constant model updates.
3. N-shot prompting
N-shot learning refers to providing examples in a single LLM API call to improve the quality of responses. A
dding one example (one-shot) significantly enhances the answer compared to giving no examples (zero-shot), while using multiple examples (n-shot) further improves accuracy without changing the model.
However, this approach is limited by the model’s context size, and frequent use can increase costs; fine-tuning can eliminate the need for n-shot examples but requires more setup time.
4. Prompt engineering
There are other prompt engineering techniques, like chain-of-thought, which force models to think out loud before coming up with an answer.
This increases the quality of response, but at the cost of response length, cost and speed.
Create a GPT chatbot trained on your data
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.
Table of Contents
Stay up to date with the latest on AI agents
Share this on:
.svg)
