Creating a Human-like Chatbot: A Step-by-Step Guide to Training ChatGPT

Paulina Lewandowska

27 Jan 2023
Creating a Human-like Chatbot: A Step-by-Step Guide to Training ChatGPT

Introduction

It's difficult to create a chatbot that can have appropriate and realistic conversations. The GPT-2 model, which stands for Generative Pre-training Transformer 2, has been refined for conversational tasks after being trained on a vast amount of text data. In this post, we'll go through how to train a ChatGPT (Chat Generative Pre-training Transformer) model so that it may be adjusted to comprehend conversational cues and respond to them in a human-like manner. We'll go into detail about the crucial elements in this approach and how they help to produce a chatbot that can have conversations that flow naturally.

How ChatGPT was made?

ChatGPT is a variant of GPT (Generative Pre-training Transformer), which is a transformer-based language model developed by OpenAI. GPT was trained on a massive dataset of internet text and fine-tuned for specific tasks such as language translation and question answering. GPT-2, an advanced version of GPT, was trained on even more data and has the ability to generate human-like text. ChatGPT is fine-tuned version of GPT-2 to improve its performance in conversational AI tasks.

Training ChatGPT typically involves the following steps:

Collect a large dataset of conversational text, such as transcripts of customer service chats, social media conversations, or other forms of dialog.

What to bear in mind while doing this?

  • The dataset should be large enough to capture a wide variety of conversational styles and topics. The more diverse the data, the better the model will be able to handle different types of input and generate more realistic and appropriate responses.
  • The data should be representative of the types of conversations the model will be used for. For example, if the model will be used in a customer service chatbot, it should be trained on transcripts of customer service chats.
  • If possible, include a variety of different speakers and languages. This will help the model to learn how to generate appropriate responses in different contexts and for different types of users.
  • The data should be diverse in terms of the number of speakers, languages, accents, and cultural background.
  • Label the data with the context of the conversation, such as topic, intent, sentiment, etc.
  • Be sure to filter out any personal information, sensitive data, or any data that could be used to identify a person.

Preprocess the data to clean and format it for training the model. This may include tokenizing the text, removing special characters, and converting the text to lowercase.

A crucial part of training a conversational model like ChatGPT is preprocessing the data. It is beneficial to organize and clean the data so that the model can be trained with ease. Tokenization is the act of dividing the text into smaller parts, like words or phrases, in more detail. This assists in transforming the text into a format that the model can process more quickly. An application like NLTK or SpaCy can be used to perform the tokenization procedure.

Eliminating special characters and changing the text's case are further crucial steps. Converting the text to lowercase helps to standardize the data and lowers the amount of unique words the model needs to learn. Special characters can cause problems while training the model. In data preparation, it's also a good idea to eliminate stop words, which are frequent words like "a," "an," "the," etc. that don't have any significant meaning. It's also a good idea to replace dates or numbers with a specific token like "NUM" or "DATE" when preparing data. In data preparation, it's also a good idea to replace terms that are unknown or not in the model's lexicon with a unique token, such as "UNK." 

It is crucial to note that preparing the data can take time, but it is necessary to make sure the model can benefit from the data. Preprocessing the data makes it easier for the model to interpret and learn from it. It also makes the data more consistent.

Fine-tune a pre-trained GPT-2 model on the conversational dataset using a framework such as Hugging Face's Transformers library.

The procedure entails tweaking the model's hyperparameters and running several epochs of training on the conversational dataset. This can be accomplished by utilizing a framework like Hugging Face's Transformers library, an open-source natural language processing toolkit that offers pre-trained models and user-friendly interfaces for optimizing them.

The rationale behind fine-tuning a pre-trained model is that it has previously been trained on a sizable dataset and has a solid grasp of the language's overall structure. The model can be refined on a conversational dataset so that it can learn to produce responses that are more tailored to the conversation's topic. The refined model will perform better at producing responses that are appropriate for customer service interactions, for instance, if the conversational dataset consists of transcripts of discussions with customer service representatives.

It is important to note that the model's hyperparameters, such as the learning rate, batch size, and number of layers, are frequently adjusted throughout the fine-tuning phase. The performance of the model can be significantly impacted by these hyperparameters, thus it's necessary to experiment with different settings to discover the ideal one. Additionally, depending on the size of the conversational dataset and the complexity of the model, the fine-tuning procedure can need a significant amount of time and processing resources. But in order for the model to understand the precise nuances and patterns of the dialogue and become more applicable to the task, this stage is essential.

Evaluate the model's performance on a held-out test set to ensure it generates realistic and appropriate responses.

A held-out test set, which is a dataset distinct from the data used to train and fine-tune the model, is one popular strategy. The model's capacity to produce realistic and pertinent responses is evaluated using the held-out test set. 

Measuring a conversational model's capacity to provide suitable and realistic responses is a typical technique to assess its performance. This can be achieved by assessing the similarity between the model-generated and human-written responses. Utilizing metrics like BLEU, METEOR, ROUGE, and others is one approach to do this. These metrics assess how comparable the automatically generated and manually written responses are to one another.

Measuring a conversational model's capacity to comprehend and respond to various inputs is another technique to assess its performance. This can be accomplished by putting the model to the test with various inputs and evaluating how well it responds to them. You might test the model using inputs with various intents, subjects, or feelings and assess how effectively it can react.

Use the trained model to generate responses to new input.

Once trained and improved, the model can be utilized to produce answers to fresh input. The last stage in creating a chatbot is testing the model to make sure it can respond realistically and appropriately to new input. The trained model processes the input before producing a response. It's crucial to remember that the caliber of the reaction will depend on the caliber of the training data and the procedure of fine-tuning.

Context is crucial when using a trained model to generate responses in a conversation. To produce responses that are relevant and appropriate to the current conversation, it's important to keep track of the conversation history. A dialogue manager, which manages the conversation history and creates suitable inputs for the model, can be used to accomplish this.

Especially when employing a trained model to generate responses, it's critical to ensure the quality of the responses the model generates. As the model might not always create suitable or realistic responses, a technique for weeding out improper responses should be in place. Using a post-processing phase that would filter out inappropriate responses and choose the best one is one way to accomplish this.

Conclusion

Training a ChatGPT model is a multi-step process that requires a large amount of data. The GPT-2 model with its ability to generate human-like text and fine-tuning it with conversational dataset can lead to very powerful results which might be extremely helpful in everyday life. The process of training is essential in creating a chatbot that can understand and respond to conversational prompts in a natural and seamless manner. As the field of AI continues to evolve, the development of sophisticated chatbots will play an increasingly important role in enhancing the way we interact with technology. Interested? Check out our other articles related to AI!

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!