7 Chatbot Training Data Preparation Best Practices in 2025

Chatbots use natural language processing (NLP) to facilitate human-like conversations, revolutionizing how businesses interact with customers by offering faster, more efficient, and personalized experiences. As the global market for chatbots grows with increased adoption, developing them requires large volumes of training data, either through data collection services, self-prepared datasets, or existing datasets.

See the best practices for creating datasets to train and enhance AI-powered chatbots, helping businesses effectively leverage this technology.

Datasets for training conversational AI

Notes

• If we missed any datasets, the last row of the table has a comprehensive list of free and paid datasets.
• This list is made from data gathered from the datasets’ websites.
• The quantities mentioned in the description column might change with time.

Figure 1. The global chatbot market projections for 2027¹

1. Determine the chatbot’s target purpose & capabilities

To prepare an accurate dataset, you need to know the chatbot’s:

• Purpose: This helps collect relevant data, create the conversation flow, and collect task-oriented dialog data. For instance, a chatbot that manages customers of a restaurant might tackle conversations related to:
  • Taking orders
  • Making reservations
  • Providing the menu
  • Offering recommendations
  • Taking complaints, etc.
• Medium: For example, if you need a voice bot, you need completely different training data than for a text-based bot.
• Languages: For example, multilingual data may need to be incorporated into the dataset.

Here is an example conversation of a restaurant chatbot and what type of questions it must tackle (See figure below):²

2. Collect relevant data

The next step is to collect data relevant to the domain in which the chatbot will operate. Since chatbots are machine learning based systems, data collection is one of the most important steps in preparing the dataset because that is where the data comes from. Chatbot training requires a combination of primary and secondary data, including:

• Different variations of questions
• Question-answer datasets from multiple domains
• Dialogue datasets
• Customer support data
• Written conversations and conversation logs
• Transcripts of previous customer interactions
• Emails in different formats
• Social media messages
• Feedback forms
• Multilingual chatbot training datasets
• Wikipedia articles

It is also important to consider the method of data collection since it can impact the quality of the dataset. Every method’s quality, cost, and flexibility differ, so you must align these factors with your project requirements. You can:

• Opt for private or in-house data collection if you can spare the budget and time and require a high level of data privacy. For instance, a patient management chatbot might work with sensitive data and, therefore, would be better suited to in-house collection of patient support datasets.
• If the chatbot requires a large amount of multilingual data, then crowdsourcing can be a suitable option. This is mainly because it offers quick access to a large pool of talent and is relatively cheaper than in-house data collection.
• You can avoid using pre-packaged or open-source datasets if quality and customization are important to your chatbot.

Check out this article to learn more about different data collection methods.

2.1. Partner with a data crowdsourcing service

If you do not wish to use ready-made datasets and do not want to go through the hassle of preparing your own dataset, you can also work with a crowdsourcing service. Working with a data crowdsourcing platform or service offers a streamlined approach to gathering diverse datasets for training conversational AI models.

These platforms harness the power of many contributors, often from varied linguistic, cultural, and geographical backgrounds. This diversity enriches the dataset with various linguistic styles, dialects, and idiomatic expressions, making the AI more versatile and adaptable to different users and scenarios.

Moreover, crowdsourcing can rapidly scale the data collection process, allowing for accumulating large volumes of data in a relatively short period. This accelerated data gathering is crucial for the iterative development and refinement of AI models, ensuring they are trained on up-to-date and representative language samples.

As a result, conversational AI becomes more robust, accurate, and capable of understanding and responding to a broader spectrum of human interactions.

3. Categorize the data

After gathering the data, it needs to be categorized based on topics and intents. This can either be done manually or with the help of natural language processing (NLP) tools. Data categorization helps structure the data so that it can be used to train the chatbot to recognize specific topics and intents. For example, a travel agency could categorize the data into topics like hotels, flights, car rentals, etc.

You can consider the following five steps while categorizing your data:

While classifying the data, to further improve the quality of the data, you can also preprocess it with the following 5 steps:

4. Annotate the data

After categorization, the next important step is data annotation or labeling. Labels help conversational AI models, such as chatbots and virtual assistants, in identifying the intent and meaning of the customer’s message.

This can be done manually or by using automated data labeling tools. In both cases, human annotators need to be hired to ensure a human-in-the-loop approach. For example, a bank could label data into intents like account balance, transaction history, credit card statements, etc.

Some examples of intent labels in banking:

You can also check our data-driven list of data labeling/classification/tagging services to find the option that best suits your project needs.

5. Balance the data

The dataset should be balanced and comprehensive to ensure that the chatbot is not biased toward specific topics or intents. The data should be representative of all the topics the chatbot will be required to cover and enable the chatbot to respond to the maximum number of user requests. 

For example, consider a chatbot working for an e-commerce business. If it is not trained to provide the measurements of a certain product, the customer would want to switch to a live agent or would leave altogether.

6. Update the dataset regularly

Like any other AI-powered technology, the performance of chatbots also degrades over time. The chatbots that are present in the current market can handle much more complex conversations as compared to the ones available 5 years ago.

Chatbot training is an ongoing process. Therefore, the existing chatbot training dataset should continuously be updated with new data to improve the chatbot’s performance as its performance level starts to fall. The improved data can include new customer interactions, feedback, and changes in the business’s offerings. 

For example, customers now want their chatbot to be more human-like and have a character. This will require fresh data with more variations of responses. Also, sometimes some terminologies become obsolete over time or become offensive. In that case, the chatbot should be trained with new data to learn those trends.

7. Test the dataset

Before using the dataset for chatbot training, it’s important to test it to check the accuracy of the responses. This can be done by using a small subset of the whole dataset to train the chatbot and testing its performance on an unseen dataset. This will help identify any gaps or shortcomings in the dataset, which will ultimately result in a better-performing chatbot.

Click here if you wish to learn more about how to test an AI model.

Further reading

• Chatbot Pricing – Complete Guide
• Chatbot vs ChatGPT: Differences & Features
• Audio Data Collection for AI: Challenges & Best Practices
• Data Labeling For Natural Language Processing (NLP)

External resources

• 1. Chatbot market revenue worldwide. Statista.
• 2. Drive Business Efficiency at Scale with Generative AI | Jio Haptik.