How To Train ChatGPT On Your Own Data

How To Train ChatGPT On Your Own Data

ChatGPT, which OpenAI's powerful language model drives, has significantly changed everyday interaction with artificial intelligence-driven bots.

For example, if you are operating a small business and plan to use ChatGPT for business planning, how much could ChatGPT be aware of the inner workings of your business dynamics? 

To what extent should the ChatGPT know about your products and company? What kind of personal data will ChatGPT have access to if you use ChatGPT to generate personalized documents.

This time, it is very likely that Chatbots will not have information to solve your problem, which means that they need some more data about you or your company. Data supplies the fuel for ChatGPT, so without sufficient data on any topic, ChatGPT is just a book with some key chapters missing.

If you train ChatGPT on your data, you unleash even more capabilities since you can do customization by domain areas, step up its efficiency, and make it respond to the specific requirements you have. But how?

Well, there are two efficient ways to do this: 

  1. Using ChatGPT Custom GPTs

  2. Using's Chatbot

We'll cover both of them in detail so that you can decide which path to take.

What Exactly is a Custom-Trained ChatGPT AI Chatbot? How Does it Work?

Customized-Trained ChatGPT AI Chatbot is an intelligent chatbot created by OpenAI that uses advanced AI techniques to understand human language and generate human-like text responses using specific data or instructions. 

These chatbots learn to respond to common queries, commands, and other topics that may interest a business using custom data sources like text documents, FAQs, knowledge bases, or customer support data. Allow ChatGPT to be trained with your data. 

You can create a specific AI chatbot that completely internalizes almost everything about your business and can deliver appropriate and accurate input for customer demands. 

These chatbots are customized and keep on learning and adapting themselves continuously. Therefore, they will be capable of keeping up with industry trends as the business evolves.

The process of creating a custom-trained ChatGPT AI chatbot involves several key steps:

  • Data Collection: 

Important data is retrieved for model development, such as customer questions, product descriptions, or FAQs, and a particular dataset is formed based on the specific usage. We use this data set as the sample data to train this chatbot.

  • Pre-processing: 

The data compilation gets a pre-processing that merges cleaning, formatting, and structuring the text to ensure consistency and eliminate background noise. This measure discards data that do not reflect the reality.

  • Fine-tuning: 

The pre-trained ChGPt model is fine-tuned using the pre-prepared dataset. In fine-tuning, a model will learn to produce contextually meaningful replies to the text input based on the information and patterns from the dataset.

  • Evaluation: 

A custom-built chatbot is tested and evaluated using several metrics: response coherence, relevance to the questions, and fluency of its natural language output. This is done to ensure that the queries are of high quality and meet the necessary conditions.

  • Deployment: 

The chatbot is put through rigorous tests to check if it meets the assumed performance, after which it is rolled out onto the desired platform for users to interact.

Preparing The Training Data: Crucial Step To Train ChatGPT To Your Data

Training a chatbot like ChatGPT involves different steps, with data preparation being very critical. Properly pre-processing the data makes the model learn efficiently and produce coherent responses. 

We'll lead you through all the data preparation steps required to run custom training ChatGPT, explaining and discussing these steps in depth.

Data Collection: 

The initial step is to create a broad set of conversations (a dataset of conversational data) that will be used for training. 

This data set will be the most effective if it covers as broad a range as possible of topics and styles of that course to which our chatbot will be deployed. The data can be captured from several sources, including customer support history, reviews of social media posts, etc.

Data Cleaning: 

Such raw conversational data commonly include some useless info, mistakes in grammar, and other unimportant nuances. Cleaning the data involves:

  • Removing duplicates.

  • Proofreading and checking the word usage and sentence construction.

  • Define the norms of the dialogues, thus preventing misunderstandings.

The vital part of this is to make it clear and accurate so that the quality of the training data is to be improved, which will result in better chatbot performance.

Data Pre-processing:

  • Tokenization: 

Firstly, each sentence must be broken down into individual tokens. Therefore, word and subword levels are feasible for the model preparation. We can use libraries like NLTK or spaCy to carry out the tokenization.

  • Lowercasing: 

While making text in lowercase, it will standardize all words. Therefore, words with different cases become standard.

  • Removing Stopwords: 

The so-called stopwords are words like "and," "the," and "a, which" do not add much flavor to the point here. In it, they remove the dimension of our data and usually use this way to accelerate model predictions.

  • Handling Special Characters: 

The manipulation of these special characters, specific emojis, punctuation marks, URLs, and others should be managed appropriately based on the relativity of the chatbot. For instance, emojis should be used as an option to express something if they are a part of the sentiment analysis in the chatbot.

Data Augmentation: 

The extent of the diversity, as well as the performance of the model, could be improved by using data augmentation approaches. 

It entails the creation of novel examples or training instances by paraphrasing the present ones, using synonyms, or making slight modifications to the existing ones. On the other hand, there is a need for artificially generated data to preserve its semantic and contextual integrity.

Data Splitting: 

After data cleaning and pre-processing, the next stage is segmentation into training, validation, and testing sets. 

The training set is where the model learns from, the validation set is where hyperparameters are tuned, training progress is monitored, and the testing set is where the final performance of the model is measured.

Formatting for Training: 

The data needs to be formulated in an appropriate input-output fashion to train the model. To illustrate, in ChatGPT, we have a model based on a sequence-to-sequence approach, with each training example composed of an input sequence (user query) and an output sequence (bot response). 

Strings of tokens or indices equivalent to the words in a vocabulary often symbolize these backbones.

Handling Special Cases: 

It depends on the particular situation of the chatbot, and such exceptions as addressing unusual words, infrequent objects, or vocabulary particular to the area of focus may have to be considered. 

This can be achieved by either manual intervention or domain-specific pre-processing techniques that are responsive to the data characteristics of the specific domain of the application.

Balancing the Dataset: 

Balancing a dataset among the classes or topics is a crucial issue, and it should be considered if a chatbot needs to respond effectively to a wide range of queries. 

Asymmetric datasets can result in inaccurate models that fail to identify lesser-represented classes.

Quality Assurance: 

Some checks on the prepared dataset are critical regarding training and validating data. This involves checking a sample of the data to ensure that it satisfies the standards of data quality and that the topic of the conversation is related and readable.

Step-By-Step Guide: How to Train ChatGPT with Your Data Using Wonderchat

Here's how Wonderchat can help you whenever you need to train ChatGPT with your data:

  • Wonderchat is very easy to use. It takes just a couple of minutes to create the first chatbot.

  • Wonderchat is the secure option for training ChatGPT. It relies on OpenAI API and promises not to utilize your data to train the AI.

  • Wonderchat can serve both business and personal needs. With a chatbot, you can train it for a business purpose, such as being a customer service agent, or for individual purposes, like helping write personalized documents, such as emails, cover letters, reports, or resumes.

  • Wonderchat allows you to add your trained bot to your website effortlessly. Provide an embed code and place it anywhere you want the chatbot to appear for visitors.

  • Unlike Custom GPTs, you can train a chatbot for free with Wonderchat and only get a subscription when necessary.

Here's how to get started with training your Chatbot on Wonderchat.

Step 1: Sign Up and Create a Chatbot

  • Create your profile by signing up for Wonderchat at this link.

  • Initially, you will only be asked to provide an email and password.

  • On reaching the sign-in page, you will find your bot creator screen after completing the registration process and logging in. On the first page, you should click the New Chatbot option.

Step 2: Add Data Sources to Your Chatbot

The next step, which is coming, is to add the sources of data to the chatbot. 

In just a few clicks, you can incorporate Wonderchat into the process by uploading your training data, extracting data from your existing website, typing the dataset on your own, or using the built-in Notion integration.

  1. To integrate data from your computer, you must go to the file uploading feature, pick a particular file you saved on the device, and click Create chatbot.

  2. To give manual or typical data from scratch, or better still, use the text area on the left sidebar to type the text, and then tap on the Create chatbot button once you are through.

  3. To train the website, click on this item in the left column, then give the address in the text area in the middle and click the 'Fetch links' button. If the process is finalized, Press the 'Create chatbot' button.

  4. To manually add questions and answers to your chatbot, check the 'add' to input fields for questions and answers; on the left sidebar, click the Q&A make the respective fields for questions and answers, and press 'Create chatbot.'

  5. To incorporate your Notion data, click the Notion on the left sidebar to provide us with your Notion account and enable us to use it as a data source for your chatbot once you press the Create chatbot button.

  6. Once you've decided to use a data source or even a mixture of data sources to let your chatbot come to life, you will land on the chatbot page, where you will have the opportunity to talk with your chatbot.

  7. Choose Settings at the top of a page to set up the most basic settings. Alternatively, click Embed on-site for the script code you should put on your site to embed your chatbot.

  8. Once created and through the platform, you can talk to the bot directly or through any website or channel where the chatbot is embedded. This helps you to speak with the personalized chatbot in your preferred environment, which gives a more natural flow.

Step-by-Step Guide For How To Train ChatGPT with Custom Data

Install Python: 

Start by installing Python on your computer. Download it from the official Python site and run the setup dialog to ensure the "Add Python.exe to PATH" box is checked during installation.

Upgrade Pip: 

Off-the-shelf, Upgrade Pip is the command-line tool that installs the Python packages from Terminal on Windows and macOS.

Install Essential Libraries: 

Include the mentioned libraries for training chatbots. You will be dealing with the OpenAI library for the Large Language Model (LLM), the library LlamaIndex for connecting to your knowledge base, PyPDF2 for parsing PDF files, PyCryptodome and Gradio that will give you an interface to interact with the AI chatbot.

Get Your OpenAI API Key: Open AI, malo aŭ ne, kreu kontonenu (Supreno lubvaAI lipotac), sei ni, cubaj por pli gabla. To access your API key, sign in with your account by clicking on the top-right corner of the profile, then, from the drop-down menu, choose "View API keys."

Prepare Your Custom Data: 

Collect and organize your data for ChatGPT to train it to fulfill the needs of your custom-built chatbot. Such data will be crucial in instilling a model with AI training that is relevant to your business or domain.

Create a Script:

  1. Build a Python program to train the AI bot with your data as data.

  2. Open up the editor of choice, like Notepad++ or VS Code, to write the code and then save the file as "" in the location of the folder containing the data files.

  3. Replace the placeholder text in the code with the actual API key.

Run the Python Script: 

Here, the terminal script educates the AI bot on your specialized data. This will give an index.json file that looks quite like our trained model. 

You can communicate and interact with your personalized ChatGPT AI learning robot using the URL displayed after you finish the lesson.

Final Words

At this point, you know how to train ChatGPT to perform tasks specific to your data so you can quickly build a capable chatbot for whatever you require it to do.

With Wonderchat, you can create personalized chatbots, choosing a less complex method or any kind hassle-free.

With Wonderchat, your website becomes even more capable, and you can now reach heights of customer engagement that you had never imagined. Create your chatbot for free via our website now!