In the early days of chatbots, the training data for a chatbot was typically a set of FAQs. This made it easy for the chatbot to learn how to respond to common questions
However, as chatbots became more sophisticated, the training data became more complex. Today, chatbot training data can come from a variety of sources, including: -Frequently Asked Questions (FAQs) -Online customer support forums -Online customer reviews -Social media -Chat logs -Sales data The training data for a chatbot must be carefully curated in order to ensure that the chatbot is able to learn from it and provide accurate responses
The data must be relevant to the chatbot’s purpose, and it should be representative of the customer base. Once the training data has been collected, it must be processed in order to be used by the chatbot
The processing step can include a variety of tasks, such as: -Labeling data -Cleaning data -Splitting data into training and test sets After the training data has been processed, it can be used to train the chatbot
There are a variety of ways to train a chatbot, but the most common method is to use a supervised learning algorithm. This algorithm will learn from the training data and produce a model that can be used to make predictions.