As the market for chatbots is getting huge, the ways to make chatbots are also increasing. There are several ways to build a chatbot, but the most important are:
What do you want your chatbot to do?
What business goals do you want your chatbot to achieve?
How satisfying does the chatbot perform in front of your customers?
All these questions need to be answered before building a chatbot.
I've already discussed a few topics, like how to determine whether your chatbot is brilliant enough to understand your customers in my previous blog.
Is Your Chatbot a Learning Champion?
If a chatbot is brilliant, then learning becomes a distinguishing trait of the chatbot. An intelligent chatbot is one that learns all the time in order to improve its performance. The modules in a chatbot include user modeling modules and natural language understanding modules, which can perform better by continuously learning. Machine learning (ML) algorithms and human supervisors enable the chatbot to learn. ML techniques like reinforcement learning, supervised learning, and unsupervised learning can be leveraged to ensure the AI chatbot becomes a good learner. The ability to learn is a key factor in creating a brilliant chatbot.
What Do You Want the Chatbot to Perform?
Infusing the brilliant quotient into your chatbot also depends on what you want your chatbot to do. You can either make the chatbot help the user or you can make it collect information from the user. A chatbot acting as a helper is considered to be more intelligent than a chatbot serving as a collector. The helper chatbot interprets what the user is saying and performs the task for the user. The intelligent chatbot could help the user buy products, seek information about cars, or even book a hotel room. A collector chatbot becomes intelligent when it responds by collecting information from the user and presenting it in the most appropriate way to serve the user’s purpose.
What Is the Model for a Brilliant Chatbot?
A chatbot based on the retrieval-based model works on the concept of predefined responses. The chatbot picks appropriate responses from the repository stack, which is based on the context and query raised by the user. Generative models built using machine translation techniques come with the ability to generate new responses right from the get-go. Generative models enable longer conversations in which the chatbot deals with several user queries. Though deep learning techniques are leveraged for building both these models, generative models seem to draw more power than their counterpart.
How Do You Want the Conversation With the Chatbot to Progress?
If you do not want to limit the conversation to a single goal or intention, then the open domain will be the right fit. In this case, the conversation can take in different directions and include different topics. In turn, the AI chatbot must have the knowledge to create responses for queries involving various topics.
Conversations happening in social media come close to the open domain category. On social media, the conversation is not narrowed down to a single topic, as the conversation goes in different directions.
When you want to limit inputs as well as outputs, closed domain is the best choice. The closed domain category works well for the chatbot that's built to achieve specific goals. Sales support systems fall into this category because the topic doesn’t veer off in other directions.