The appearance of the ChatGPT chatbot from OpenAI can be considered a small but revolutionary step in the field of technology. This language model, generating texts, responses, code, and more, has become a convenient tool for many professionals. From creating a convenient tourist route to developing a marketing strategy for a company, Chat GPT can do it all.
However, the chatbot’s responses do not always match user queries. This leads to the idea of additional training of the language model on your own data. Yes, this possibility does exist, and this article is entirely dedicated to how to train Chat GPT so that its responses fully meet the needs of a specific user, whether a student, marketer, or company executive.
Capabilities of ChatGPT
The abbreviation GPT stands for Generative Pre-trained Transformer. In other words, it is a generative pre-trained transformer, which represents a large-scale neural network trained on a vast amount of data. This is the “pre-training.” As the language model continues to work, it keeps learning and evolving. While its capabilities are limited, even without additional training, the neural network can do a lot. It can predict the next word in a sequence by analyzing the sequence of words and assigning probabilities to the next word for each received sequence.
Yes, this is the principle of ChatGPT’s operation. In fact, it is a very scaled-up, refined version of T9, provided with a vast amount of data. That’s why the language model can:
- model natural language in the desired style (formal, friendly, humorous, etc.);
- remember the context of the conversation;
- generate text of any format, size, and content.
But what if you train the pre-trained AI on your data? In this case, you get a personal assistant that saves you time and resources when solving various tasks. Plus, your version of the GPT personal assistant can become a source of income if you place it in the GPT store. Currently, in the store, there are over 20 thousand Custom GPTs. Each version is trained to solve specific tasks such as SEO queries, technical support, health and fitness support, travel recommendations, education, participation in conversations and tabletop games, marketing and advertising, business, trading, programming, translations, and content generation.
Essentially, you can train Chat GPT for anything; its capabilities are limitless. And the best part is that every user with access to this AI can do it. Yes, the process is not the easiest, but following instructions will lead to success. Training Objectives and Tasks for ChatGPT Before diving into how to train Chat GPT, let’s address another aspect: the goals and tasks of its training. A neural network is an effective tool for working on many tasks, but you need to understand what it will be specifically used for. Otherwise, you might end up like hammering nails with a microscope—things might work, but not quite right.
Training Objectives for AI can include:
- Improving content quality—its informativeness, style, coherence, etc.
- Performing new tasks needed specifically for you.
- Personalizing the chatbot—a good solution for customer interactions.
- Optimizing the AI’s performance—increasing its speed and efficiency.
- Working with tables, data, computations.
It’s easy to determine these objectives, but it’s a bit more challenging to understand the tasks needed for further training. These tasks include:
- Data preparation for training.
- Choosing the language model.
- Training.
- Testing, analyzing results.
- Integrating the retrained model into the existing system.
Data Requirements for Training ChatGPT
Data is the foundation of training any neural network. The AI’s output entirely depends on the data it was trained on. If the neural network provides incorrect answers, it’s not its fault—there might have been incorrect information in the data set. Remember that language models understand context but not the meaning of what they generate. They will not understand the meaning of what they generate in the existing format; other technologies are needed for that.
Thus, the data requirements for retraining AI are quite strict:
- Type: Data in text format (text files) are used for retraining, but HTML or JSON formats can also be used.
- Volume: The more data, the better. ChatGPT is trained on massive datasets, and yet its capabilities are still limited. If you’re interested in maximizing GPT’s efficiency, provide it with the maximum amount of data you can. Naturally, the amount of information depends on your goals, but there can never be too much.
- Quality: Texts should be free of typos, grammatical or spelling errors. The information must be correct, accurate, and relevant to your goals. Additionally, the information should be diverse in terms of styles, topics, opinions, etc. Also, data should adhere to ethical norms—there should be no offensive, unacceptable, or discriminatory content in the data. Not to mention content prohibited by the law.
- Validity: All data must reflect reality and be impeccably accurate—otherwise, the AI will generate texts with factual errors.
- Structure: It entirely depends on the training tasks. For example, if your goal is to improve GPT’s translations, the loaded data should consist of identical texts in multiple languages. And if you want to train the AI to write texts in different styles, your data set should consist of corresponding examples. Or if you want a personal secretary, the data should contain information about your lifestyle, daily routine, preferences, habits, business contacts, work peculiarities, and more.
Starting Chat GPT Training
Let’s move on to the main section of our article: how to train Chat GPT for your needs. There are two ways to do this. The first is for those familiar with Python, tokens, samples, input-output, etc. If these terms sound like magic, let’s jump straight to the second method, which does not require programming knowledge.
This method involves using builders like SocialIntents or BotSonic or plugins like ChatGPT Plus. You can also rely on user instructions for ChatGPT.
ChatGPT Plus Plugins
To use them, you need a ChatGPT Plus subscription. After installation, follow these steps:
- Choose the GPT-4 model.
- In the bottom-left corner, click on the menu (three dots).
- In the menu, select “Beta Versions.”
- Enable plugin support.
- Create a new chat.
- Click on “Plugins.”
- Open the plugin store.
In the store, select the plugins that suit your needs. These can be plugins for reading links, working with data from Google Sheets and Docs, data from PDFs, and more. The store also has plugins for video work—like Video Insights.
Select a plugin in the store and enable it. Then provide the AI with a link to the data source and describe the task.
User Instructions
This method is akin to basic training and works well for creating personal assistants for daily tasks, business, tourism, etc. You can even activate user instructions for the free version of ChatGPT:
- Open Settings in the app, go to the “Account” menu. In this menu, select User Instructions and activate them.
- Enter a chat, click on your name, select User Instructions from the list, and click OK.
- Enter your instructions for the neural network. You can input any training data in the field, along with a detailed description for the AI’s response format.
- Save the settings for this chat. User instructions can be disabled for new chats.
Builders
These are more advanced tools for retraining language models and creating custom chatbots. Their functionality is similar, so after learning how to use one builder, you can easily work with the others.
Let’s look at how to train Chat GPT on your data using the BotSonic builder. This builder works with text files and links, allowing you to configure many aspects of the future chatbot. This includes the bot’s name, branded colors, logo, greeting, button designs, examples of common user requests, and much more.
Using the builder is simple:
- Create an account on Writesonic, which includes the BotSonic builder.
- Go to BotSonic.
- Open the “Data Upload” section to load your files or links for training.
- Customize the chatbot’s personalization.
- Wait for the process to complete and click “Refresh.”
After the training is complete, the builder will generate a unique API key. Insert this key into your website’s code.
Testing and Evaluating Results
To test the retrained chatbot, start working with it. The method of evaluating results depends on your training goals. For example, if the goal of retraining was to improve text generation quality, you can assess their informativeness, context relevance, required style, etc. A useful approach is comparing two texts generated from the same task—before and after model training.
During testing, it’s essential to identify and analyze any errors made. By identifying the root causes of errors, you can train the model more effectively.
For a more effective analysis, various evaluation metrics are used, testing on data not used for training. Visualization methods, such as topic modeling, can precisely assess the AI’s response structure, content, and other parameters.
