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How to Train ChatGPT On Your Own Data (7 Easy Steps)

Published in Mar 14, 2024

13 min read

Conversational Search
AI Powered Website search & chat

Intelligent automation integrated into websites and document workflows is more than just an emerging trend; it is a revolutionary change. ChatGPT is a critical part of redrawing a connection to your audience.

In this article, we'll go over how to train ChatGPT on your own data in traditional ways, and ways you haven't heard before.

What is ChatGPT Custom AI Chatbot?

A custom AI chatbot understands and can communicate specific questions, directions, or subjects is designed to cater to a particular dataset or experienced guide. You can customize your own chatbot to incorporate AI into your own data or knowledge base. This may involve including your website, text documents, FAQs, knowledge bases, or customer support-related records.

The unique aspect of this custom chatbot is its ability to learn and adjust as time passes. It can easily retain information for a long time and stay current with industry changes as the organization grows its scope.

Build a Custom AI Chatbot With One Click Using

Creating an AI chatbot with Denser AI is a one click integration, and takes no coding experience or skill at all. All you need to do is give your Denser Bot access, and it does the rest.

There are a few use cases with, a conversational AI website chatbot, file search, database and technical document chatbot, and website lead generation.


Below are the easy steps to integrating your Denser AI chatbot onto your own data. We'll take a look at website integration and file/document integration. Even if you have no prior coding knowledge and experience, following the easy steps below can help:

File Search and Chat Setup

Create a chatbot that you can input your own data and knowledge base into. Communicate with this AI chatbot about your custom data, too. Denser_Sign_Up_Image.png

Step 1: Sign Up For

Sign up for Start a free account, you'll be granted 1 Denserbot, and a limited number of free queries per month.

The freemium version is the best chance to use the application at no cost. Or [book a demo] ( a team member.

Step 2: Create a New Chatbot


Step 3: Choose Files


Step 4: Upload Your File


Upload your file to, and hit "Build Now" to begin the Denser Bot creation on the custom data that you uploaded. This can also be considered training data.

Step 5: Start Using Your Knowledgeable Denser Bot


Put your Denser Bot to the test on your own data. Ask questions to the chatbot, and get specific answers about your documents and custom database. The more training data and user inputs your feed into Denser, the more trained the AI chatbot will become.

Conversational AI Website Chatbot Setup

Follow steps 1 and 2 from above to create your conversational AI chatbot on your website data, for the public.

Step 1: Select "Web"


Step 2: Type In Your Website and Hit "Build Now"


Step 3: Wait Only Minutes For Your Site to be Crawled


Step 4: Short Snippet Integration Into Your Website

Once you have created your Denser Bot, you can test it out within our platform, then linked below is how you integrate it into your website or app. Follow this full integration guide. Snippet_Integration_Image.png

How to Optimize Your Training Data in 4 Steps

Preparing your training data is essential to ensure your ChatGPT model's performance operates effectively within these setups. Below are key strategies to refine your training data for optimal results:

1. Collect & Input Data

Start by collecting data that mirrors the conversational tone, style, and domain-specific knowledge you aim for ChatGPT to replicate. Whether it's detailed customer interactions or an insightful blog post from your website, this initial collection phase lays the groundwork for a tailored training dataset.

To train ChatGPT, you can use plugins to bring your data into the chatbot (ChatGPT Plus only) or try the Custom Instructions feature (all versions). Alternatively, if you would like to create your own custom AI chatbot using ChatGPT as a basis, using a third-party training tool can simplify the bot creation, or you can program it yourself in Python using the OpenAI API.

2. Clean and Preprocess the Data

Next, cleanse your dataset by removing extraneous information, correcting errors, and ensuring uniformity across the raw data. This data-cleaning process is critical as it establishes a clean slate for training, allowing the AI to focus on the most relevant information without distractions. Optimizing your dataset for training involves a few key steps:

  • Get Rid of What You Don't Need: Go through your data and remove anything useless for training, like off-topic chat or duplicate info. For example, if your chatbot is for customer service, you might remove all the casual conversations that don't relate to customer queries.

  • Fix Mistakes: Look for and correct any mistakes in your data, such as spelling errors or wrong facts. For instance, if you find a sentence with a typo like "This is an example," correct it to "This is an example."

  • Make Everything Match: Ensure all your data looks the same regarding how dates are written, sentences are structured, and so on. If some parts of your data use MM/DD/YYYY and others use DD/MM/YYYY, choose one format and stick to it throughout.

  • Organize and Label: If you have long documents, break them into smaller parts that are easier to handle. If sections need explanations, add notes. This could mean dividing an extended customer service transcript into separate questions and answers and labeling each with tags like "query" and "response."

3. Ensure the Quality of Your Data

The focus on training data quality should always be on quantity. Ensure that your dataset is accurate and versatile and can represent the different scenarios and queries that a ChatGPT chatbot would encounter.

4. Format Custom Data

Selecting the Ideal Data Format

Picking the most suitable format for data transmission data, like JSON, is essential in the training and deployment phases. Such formats are often preferred because they have a structured style of data preparation. Data models can easily lead to streamlined interpretation and training experiences.

Structuring Data for Full Conversations

Structuring the single input-output sequence is vital when ChatGPT chatbot projects are intended to produce complete conversations. Such a conversational AI system allows the model to learn to transition between messages coherently and contextually from the beginning to the end of the conversation.

Structuring for an input output format when training chatGPT or any conversational ai systems is necessary in a machine learning enviornment. Over time and enough training, your AI bot will generate responses in a virtual assistant format, understanding the user intents.

Crafting Conversational Pairs for Training

You have to organize your data into pairs of dialogues to receive clear feedback on real-world scenarios with conversations. This approach assigns a user question to the next AI reply, which resembles the regular exchange in a human conversation.

Training conversational AI models with a semantic chatbot platform like can make the process even more powerful. For example, you can upload an organization's documents or knowledge base to Denserbot. After Denserbot has crawled this data set, it will be ready to give you much more accurate and knowledgeable answers, thanks to its access to vast volumes of proprietary information.

Besides, this capacity for learning is helpful for having effective dialogues and contributing to conversations and also increases the precision of the responses; thus, communication with AI becomes easy and natural.

7 Reasons Why You Need A Trained ChatGPT AI Chatbot

Below we'll take a look at why you may need an AI bot inside of your custom data, or front facing on your website, as a virtual assistant to users.

Website Search and Navigation

Improve conversions and site interactions with users, with an AI bot that can crawl your entire website, and help users navigate and search anything on your site or about your company.

Lead Generation

In certain cases depending on the input message, you can prompt your AI website chatbot to output system messages to capture the users’ email address, which can be used for an email marketing list, or more lead nurturing from a sales team.

Allow a custom AI bot to parse pdf files with your own data, and provide semantic search and information about your custom data. This helps internal teams summarize your custom knowledge base, or perhaps understand technical documents much faster.

Customer and IT Support Chatbot

Instant answers 24/7 help customers enjoy the benefits of fast service because they can get answers at any hour of the day or night. This continuous service increases client satisfaction since customers receive timely help, which is significant for businesses in different time zones.

Customer Interactions

A custom AI chatbot enables companies to replace human effort for many repetitive workloads and use extra labor and time, thus freeing their human resources for more duties. This comprises providing the machine learning model with the usual answers to frequently asked questions (FAQs), making appointments, and processing simple customer requests.

User Experience

A key feature of a custom-trained ChatGPT AI chatbot is the ability to deliver customized conversations based on analyzing users' inquiries and providing answers that address their needs and preferences.

Such a high degree of customizability balances customer satisfaction. It creates a sense of involvement and loyalty towards your brand, as customers feel they are being treated foremost and their unique needs and requirements are considered.

Adaptive Learning

Every user's input with an AI chatbot gives them something to learn and enhance. Over time, this improves at recognizing content and replying to queries with accuracy and relevance in context. This goes on and on rounds; thus, a custom-trained ChatGPT AI chatbot is a perfect tool for your business, which can adjust to new patterns, consumer patterns, and specific industry issues.

Fine Tuning ChatGPT

You can now fine tune ChatGPT effectively by adding your own dataset relevant to the particular field - be it transcripts of customer services, technical manuals or niche articles. The model will learn and understand the intricacies of the related area, which will lead to more effective conversations.

Fine tuning can also be done easily with a tool like

How to Train ChatGPT on Your Own Data Traditionally

Training your own AI chatbot on your own data also involves several steps tailored to ensure that the chatbot fully grasps the nuances of actual data about your business or project. Here's a simplified breakdown of how to train ChatGPT on your own data:

1. Install Python

First, you must install Python, a language for writing scripts that will train ChatGPT. This is the basis for executing the training program code, ensuring it is updated to guarantee compatibility and security reasons.

2. Upgrade Pip

Pip is currently the default installer and a package manager for Python, and you'll need it to install other helpful software libraries. Updating Pip will help you have the most updated version, which is less likely to cause problems installing libraries that you will need when training ChatGPT.

3. Install Essential Libraries

Python libraries are collections of functions and methods that readily perform many tasks through writing your own code. Training ChatGPT can benefit from using libraries like PyPDF2 for parsing PDF files or PyTorch, which are tailored to various tasks. Such libraries typically come with pre-built algorithms and neural network architectures necessary for machine learning and AI work.

4. Download a Code Editor

The key to editing and adjusting code is having a good code editor. Users who work on Windows systems can use Notepad++, which is simple and useful. However, if you want something more versatile and cross-platform, use Integrated Development Environments (IDEs) such as VS Code.

Another option is Sublime Text, the preferred program for those who run on macOS or Linux because of its stylish design and robust capabilities. Selecting the correct code editor or IDE can significantly impact the production process, making script customization easier and more efficient.

5. Generate Your API Key

The first step in using ChatGPT is to get an OpenAI API key. This secret key will be a unique ID for your projects, allowing secure communication between your scripts and OpenAI's servers. This safeguard feature helps you monitor your quota of actual API key and limits and validate whether transactions are valid.

6. Choose Your Model & Create Your Knowledge Base

Choosing the appropriate ChatGPT model is essential. OpenAI offers different versions, each with unique features and trained datasets. Consider your needs, like language understanding or creative content generation, to select the most appropriate one.

Next, get the texts you will train on—they can be a database of documents or text files. Such training data will help ChatGPT understand your specific field or the type of interactions it should handle.

7. Create the Script

With everything in place, the final step is to write a Python script that brings it all together. This script will use your OpenAI API key to authenticate with OpenAI, load your data in a local URL, and then use it to train or fine-tune the ChatGPT model according to your specifications, in the same location.

The script writes the data into the trained model, the training process takes place, the trained model's final performance is applied to your tasks, and its performance is evaluated.

Build an AI Chatbot Using Your Own Data With

Train conversational AI on your own data source, using Denser. Try out a [freemium version] ( schedule a product demo today!


FAQs About How to Train ChatGPT on Your Own Data

Do I need technical expertise or coding knowledge to train ChatGPT? While having a basic understanding of AI and programming helps, the wealth of data sources and user-friendly platforms available today means anyone willing can train ChatGPT.

How much data do I need? The more, the merrier, provided it's high-quality. However, even small datasets can yield significant improvements if well-curated.

How long does training take? Training duration varies based on the amount of data and the computing resources available. It could range from a few hours to several days. averages only minutes for formatted data sets.

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