How to Create a GPT Model? A Step-by-Step Guide

How to Create a GPT Model? A Step-by-Step Guide
May 07 2024

Thanks to the introduction of ChatGPT by Open AI, the world of artificial intelligence has changed forever. The diverse capabilities of GPT or Generative Pre-trained Transformers in Natural Language Processing have made everyone looking to use them for a variety of purposes.

However, having the same GPT to do different types of tasks did not feel very efficient or effective for businesses.

That’s where businesses began looking at building custom GPT models to meet their business goals and needs.

But the big question is—how to build a GPT model.

Well, in this blog post, we explain everything you need to know about building custom GPT models, their advantages, and how to choose the right GPT models that fit your needs.

What Is a GPT Model?

GPT, short for Generative Pre-trained Transformer, is a language model revolutionising natural language processing (NLP). It is an ML model that can accurately handle various NLP tasks.

GPTs differ from traditional ML models designed for specific tasks in their ability to handle data and generate precise outputs.

Three crucial components make GPTs unique. Let’s break them down here to understand GPTs better.


You know that GPT is a generative model. But what does that mean?

It means GPT can generate new data by learning the relationships between variables in a dataset. The models learn from the training data and the relationships between the data to create original text outputs.

This ability allows GPTs to produce human-like text. That’s why GPTs are incredibly useful in language generation and creative writing.


This is the next crucial element. GPTs are pre-trained on a massive corpus of data. The data set often includes billions of words from numerous sources, such as online articles, books, academic articles, wiki pages, etc

GPTs develop a deep understanding of language and context through these pre-training efforts.

The pre-training enables them to perform tasks with minimal additional training.


The transformer architecture was introduced in 2017 as an artificial neural network to handle sequential data like text, and GPTs use this powerful architecture.

Using the network, GPTS can capture long-range dependencies and understand logical connections within the data.

This understanding is what allows it to create new, original content upon request.

As these three elements work together, GPTs function marvellously, creating new passages of content, generating images, discovering patterns, etc. These capabilities make it uniquely suitable for NLP and various business applications like creating chatbots, customer service bots, etc.

What Are the Benefits of Using GPT Models?

The benefits of using GPT models are straightforward and limited. And they don’t seem that revolutionary. Yet businesses are investing millions of dollars in generative AI consulting and building custom GPTs for their business.

It only means one thing—there is more to these GPT models than meets the eye.

Let’s try to understand the groundbreaking benefits of GPT models.

1. Utilize natural language processing capabilities

GPT models excel in handling NLP tasks with remarkable accuracy and efficiency.

The unique combination of deep learning algorithms and vast amounts of training data allows them to understand the context and recognize patterns. This capability enabled them to generate human-like responses.

All these elements make them a powerful tool for NLP applications like chatbots, language translation, and question-answering systems.

2. Enjoy efficient training

One of the standout benefits of GPT models is their efficient training process. Unlike other AI models, GPT models have significantly faster training times.

This faster training time makes quicker completion and deployment of projects possible. And this is extremely important for businesses that work on time- and resource-sensitive projects.

GPTs ensure efficiency thanks to their advanced architecture and the vast amounts of data used to train them, saving valuable time and resources.

3. It ensures cost and resource effectiveness

Custom software development projects can be expensive, which is why cost-effectiveness is a crucial factor to consider. GPT models are

GPT models offer a high level of performance at a relatively low cost. This makes them an attractive option for different types of businesses. They provide a better cost-performance ratio compared to other AI models, without compromising on quality.

This is a crucial benefit for businesses that are looking to reduce their resource and computational costs.

4. GPTs ensure better performance

GPT models have a proven track record of delivering better performance compared to other models. In several benchmark tests, they have outperformed their counterparts.

Hence, we can confidently say that GPT models are a superior choice for businesses seeking accurate and reliable AI solutions. In addition, you can also ensure better and more successful project outcomes and customer satisfaction with GPTs thanks to their high performance.

After all, no technology is worth your investment if it does not perform well.

5. You can ensure improved accuracy

Accuracy is a key benefit of using GPT models. They can help make accurate predictions and decisions due to their training in large data sets.

GPT models do it by understanding patterns and relationships within the data. Businesses rely on the accuracy of outputs as they make business and investment decisions based on the responses of GPTs.

Further, better accuracy leads to increased efficiency and productivity. As AI-powered systems provide more relevant and useful results, you can save time, effort, and resources.

6. It helps with continuous learning and improvement

This is another remarkable benefit of the GPT model. GPTs can learn and improve continuously to respond to your growing needs and deliver more accurate responses as time goes by.

You can also fine-tune and train the GPT on new data as you have it. This enables chatbots and other AI-powered systems to adapt and grow smarter over time. As chatbots interact with more users, they gather valuable insights, identify patterns, and adjust their responses to deliver more accurate, relevant responses.

As a business, this is critical for better customer service and satisfaction. Since the bots themselves learn and improve the quality of their responses as they encounter more diverse scenarios, you don’t have to manually do the training.

How to Build a GPT Model

Creating a custom GPT model involves diverse steps. You need to pay attention to each of the steps to create a highly accurate, efficient GPT model for your business.

Here is a step-by-step explanation of building a GPT for your business.

Step #1. Understand requirements

This is the first step of creating a GPT for your business.

You need to understand the purpose for which you are building the GPT. Are you going to use it for chatbots? Do you want the GPT to help you analyze data and understand patterns? Are you looking to generate insights from data?

Asking these questions will take you closer to the fundamental purpose for which you are building the GPT.

Once you have understood the requirements and defined the vision for the GPT, you can move ahead. Defining the requirements makes it easier for the next steps to follow.

Step #2. Collecting the training data

In this stage, based on the purpose of the GPT model, you need to collect training data. The more accurate and diverse the data, the better the responses given by your GPTs will be.

Low-quality training data can also make the model return low-quality responses. Hence, you need to be careful with selecting and collecting the data.

You can gather large chunks of data from diverse data sources, like books, articles, websites, academic papers, medical records, etc.

Make sure that the data represents the domain and language the model is expected to operate in. If you want the model to operate in multiple languages and domains, use data from different languages and domains.

‍Step #3: Preprocessing

After collecting the data, you don’t use the data as it is. You need to clean and preprocess it for the best results.

Several steps go into preprocessing the data.

  • Data cleaning: Here, you clean the data by removing irrelevant or noisy text, such as HTML tags, special characters, or formatting artifacts.
  • Data tokenization: This involves breaking down the text into smaller units called tokens. The tokenization process enables the model to efficiently handle a vast vocabulary.
  • Data segmentation: In this process, the tokenized text is divided into fixed-length sequences or chunks suitable for training for your GPT model.
  • Numerical encoding: Here, the tokenized text is converted into numerical symbols, and unique integer IDs are assigned to each token in the vocabulary.
  • Data formatting: Organize the preprocessed data into a format compatible with the training pipeline, such as input-output pairs or batch sequences to feed into the model.
  • Data normalization: Here, you need to normalize the tokenized text using techniques like lowercase conversion, punctuation removal, accent stripping, etc.

‍Step #4: Choosing the architecture

As there are different GPT architectures, you must choose the right one. Some of the most common architectures in use now are:

  • GPT-1
  • GPT-2
  • GPT-3
  • GPT-4

All these architectures have different capabilities, strengths, and limitations. You need to choose one of them based on the purpose of your GPT model. Also, keep in mind that each version builds upon the previous one, leading to improvements and better training.

‍‍Step #5: Pre-training

While building your GPT model, it is trained through unsupervised learning. The training is carried out using cleaned and preprocessed data.

Here, the goal of the training is to make the GPT predict the next word or token in a sentence by looking at the previous words and context given to the model. This stage is crucial in the process of building a GPT model.

For a GPT model to work efficiently this stage needs to be a great success as this pre-training is what lets the model learn the nuances of language, its semantic relationships, and general language understanding.

‍‍‍Step #6: Fine-tuning

Once the pre-training stage is over, the next stage is to fine-tune the model further. The developers use supervised learning to improve the GPT model on specific tasks or domains where the model is not performing as expected.

There could be several areas where the model needs improvement, like conversations, pattern detection, translation, etc.

In this process, the developers use labeled data and offer explicit feedback for the responses generated by the GPT to improve performance.

‍‍‍Step #7: Iterative optimization

In this stage, developers use different types of experimentations to adjust the hyperparameters of the GPT model and evaluate its performance to optimize the model.

The chief goal of the process is to make the model perform better concerning its abilities, such as text generation, linguistic understanding, task-specific capabilities, etc.

‍‍‍‍Step #8: Deployment and usage

This is the last stage of developing a GPT model. Here, the model is deployed for use. You or the intended users can use it for various purposes.

If you are planning to create APIs or interfaces for specific applications, the developers or a generative AI consulting firm can also help you with the same. Even when the model is working and performing tasks, always be on the lookout for opportunities to improve it.

Keeping such an open mind for growth will take you to numerous scenarios that you may not have foreseen. And in the subsequent updates, you can make the model even better.

How Much Does It Cost to Use GPT Models?

AI has become an integral part of running businesses and improving their operational efficiencies. Hence, developing AI tools and GPT models has become a part of the growth strategies of businesses.

As businesses are looking to create GPT models for chatbots, content generators, and data analytics, one question pops into our minds.

How much does it cost to use GPT models? Well, let’s find out.

Factors affecting the cost of use.

Several factors affect the cost of the use of GPT models. Each of these factors can drive up the cost of use.

‍‍‍‍1. Size of the model

This is a no-brainer. The larger the model, the higher the cost of use. Large GPT models typically have more parameters which require more computational power to work. Larger GPT models also offer better responses. Hence, the cost will be higher for quality and accurate responses.

‍‍‍‍2. Resources for computation

A huge part of the cost of AI comes in the form of training and operating the GPT models. You need training and deployment infrastructure like cloud servers to make the GPTs work. To keep the servers cool and always functioning, you also need electricity.

‍‍‍‍3. Cost of training data

For the GPT models to deliver excellent performance, you need large quantities of high-quality data. Collecting the required data for training can be expensive, especially if the data is proprietary, hard to get, or academic.

‍‍‍‍4. Hiring technical talent

This is another cost factor. You need skilled and experienced technology professionals to work on your project. They must have the qualifications, technical insights, and vision to make the GPT model work for you. And that’s going to cost you considerably.

How to Calculate the Cost of Using GPT Models?

Based on the type and purpose of the GPT model you use, calculating the cost of use changes. Let’s look at two different ways to determine the cost of GPT models.

For chatbots

Imagine you are using the GPT-3 model to create a chatbot that can handle 1,800 conversations a day. For 1,800 conversations to happen, let’s say you need 18,000 tokens at USD 0.001 per token.

Assume that you are using a GPU from Amazon or Google Web Services at USD 2 per hour and that each conversation takes 1 minute.

The total GPU use = 1800 minutes

In hours, it is equal to 30 hours

Total GPU cost = time x cost per hour

Total GPU cost = 30 x 2 = 60 USD

Total cost of use = Total cost of tokens + Total GPU cost

Total cost of use of the chatbot = 18 + 60 =78 USD per day.

For GPT models that generate content

You are creating a content generation GPT model that runs on GPT 4. You plan to create 1,000 articles every month. Assume that each article uses 100,000 tokens at USD 0.001 per token.

Cost of tokens = No. of total tokens x cost per token

Cost of tokens = (1,000 x 1,00,000) x cost per token

Cost of tokens = 10,000,000 x 0.001

Cost of tokens = USD 10,000

Now, it is time to calculate the cost of computing.

Assume that you are using a GPU from Amazon or Google Web Services at USD 1.5 per hour and that each article takes 2 hours to complete.

The total GPU use = 2 x 1000

In hours, it is equal to 2000 hours.

Total GPU cost = time x cost per hour x 30 days

Total GPU cost = 2000 x 1.5 x 30 = 105,000 USD

Total cost of use of the GPT model = Total cost of tokens + Total GPU cost

Total cost of use of the GPT model = 10,000 + 105,000 =115,000 USD per month.

Use Cases of GPT Models

There are several use cases for GPT models in a business. You can build custom GPT models for various purposes.

Here are some of the most common use cases of GPT models.

  • GPT models help translate human knowledge into machine-readable format to make it easier for machines to comprehend the meaning of sentences.
  • You can use GPT models to generate content for web pages, blogs, articles, guides, etc. This leads to faster content creation with minimal effort.
  • When combined with computer vision systems, GPT models can identify, collect, and recall, unique elements in an image, such as faces, colours, and landmarks.
  • AI chatbots powered by GPTs can understand and respond to customer queries with human-like precision. This helps you provide instant, accurate customer support economically.
  • GPT models excel at accurately translating text between various languages without losing the original meaning and context. This helps facilitate cross-cultural communication.
  • Many businesses use GPT models to generate code snippets based on developer input. This helps speed up the coding process, reduce errors, and contribute to faster development.
  • GPT models can provide personalized tutoring and learning assistance. Hence, it can be used to generate educational content tailored to a learner’s needs to make education engaging.
  • Writers can use different GPT models for creative suggestions and to overcome writer’s block. Some models are even capable of generating entire stories or poems, enhancing productivity.

How to Choose the Right GPT Model for Your Needs?

The GPT model you use for your project determines the quality of your output and the decisions you make based on these outputs. Therefore, closely assessing and choosing the most suitable GPT model for your business is crucial.

Here is how you can choose the suitable GPT model for your needs

‍‍‍‍1. Assess the complexity of the task

The complexity of the tasks you want the model to handle is the first thing to consider. Certain GPT models are suitable for complex tasks while others are not.

For example, GPT-1 is suitable for simple tasks like customer inquiries, customer service, etc. But, for more complex language generation tasks like deep analysis, recommendations, or story generation, GPT-3 is a suitable choice.

Picking the right model also determined the quality of the output.

‍‍‍‍2. Consider the type of language

This is another critical element. You need to choose a GPT that can handle the language you will be working primarily with the GPT.

While most GPTs will work with languages, you may need higher GPTs like GTP-3 or 4 to handle multiple languages. This also ensures that you can answer more queries in different languages.

If you are a business handling multiple languages, higher models are the best.

‍‍‍‍3. Check the size of the data set

To train your GPT model, you need data. Therefore, if you have large datasets, you need a more capable GPT model with the capacity to learn from large data sets.

On the other hand, if you have limited data resources, you can use lower GPT models.

Choosing earlier versions like GPT-1 or GPT-2 also makes the entire operation more efficient and economical.

‍‍‍‍4. Check computational resources

Depending on the GPT models, the computation power they need to function efficiently changes. While GPT-1 and GPT-2 do not require huge computational power, higher models like GPT-3 and GPT-4 need lots of power.

Therefore, check how much the model you want to choose needs to perform the tasks assigned to the model.

‍‍‍‍5. Look for scalability and future needs

Newer iterations like GPT-4 emerge every other month. While these newer versions offer enhanced performance, they need even larger datasets and more computational resources to work.

Therefore, always look for details about the specific capabilities and requirements of each model. Doing this will help you make an informed decision about the model suitable for your business.


If used intelligently, GPT models can increase your business’s efficiency and productivity. However, building these models as you need is a lengthy and complex process. Hiring companies that offer tailored GPT development and generative AI consulting services is crucial to ensure you get the most suitable model for your business needs.

Fullestop has been at the forefront of AI development and research even before Open AI made a significant impact in the industry with ChatGPT. Our experienced developers have offered advanced and tailored generative AI consulting services for leading businesses in India and abroad. This makes us one of the best names in the industry to help you build your GPT model.

At Fullestop, we focus on your requirements and what you are looking to gain from the project. This focus has helped us work with leading businesses on their AI generative projects and meet their goals successfully. If you would like to know more about our services, speak to our client support team.

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