A chatbot is a software that uses chat to imitate human-like conversations with users. Chatbots can be simple, like those that answer simple questions with a single line of code, or they can be complicated, like full-fledged digital assistants that learn and get better as they get more information. As technology and artificial intelligence improve, many new things are being made, including new kinds of chatbots. In this article, we will look at the various types of chatbots and provide examples of each.
Rule-based chatbots, which are often called "click bots," give users a set of questions and answers. If a user asks a question beyond these pre-designed questions, the bot cannot respond. It's important to make the rule-based chatbot flexible by adding photos, GIFs, music, and videos, among other things. Personalization (asking for a name or first name) and a lot of answer options make the experience better. Rule-based bots are cheap and easy to build.
Natural language processing (NLP) chatbots are a type of AI chatbots that will be able to mimic communication with human users. They may have conversations with customers using a number of channels, including text-based messaging apps, voice assistants, and websites with real-time chat features. NLP Chatbots are used in many contexts, from customer service and marketing to the arts and recreation. You can rely on them to take care of mundane, repetitive activities, provide you with answers and help you solve problems, and make your company and your life easier to navigate. Chatbots powered by Natural Language processing are now able to comprehend and respond to more human-like inquiries.
NLU chatbots are a type of AI chatbot that use NLP techniques to interpret and understand human language. Unlike rule-based chatbots, which rely on pre-determined responses to specific inputs, NLU chatbots can understand the intent behind user inputs and generate relevant responses. This allows them to handle a wider range of queries and respond more effectively to user requests.
NLU chatbots can be trained on large datasets of language data to identify patterns and relationships between words, phrases, and sentences. This training process helps the chatbots understand the context of a user's input and generate appropriate responses. With continued use, NLU chatbots can also learn and adapt to new language patterns and improve their accuracy over time.
An ML chatbot is an AI chatbot that uses algorithms for machine learning to come up with answers. Unlike rule-based chatbots, which have answers that are already set, ML chatbots can come up with answers based on the information they get. This lets them handle a wider range of user questions and respond in a way that seems more natural and human. ML chatbots usually use supervised learning algorithms. This means that the chatbot is trained on a large dataset of example inputs and outputs, which teaches it to recognise patterns and relationships between inputs and outputs. An ML chatbot can also get better and more accurate the more it is used because it is always learning from how people interact with it. ML chatbots are used a lot in customer service, marketing, and other areas where they can answer users' questions quickly and easily and help human operators with difficult tasks.
Hybrid chatbots combine rule-based and machine learning-based AI chatbots. It's more effective and adaptable since it incorporates both sorts of chatbots. Moreover, hybrid chatbots use pre-defined rules to answer commonly requested questions or guide users through a procedure. The hybrid chatbot can respond dynamically to sophisticated or open-ended inquiries using machine learning methods. Also, hybrid chatbots may improve the user experience by combining rule-based chatbots' efficiency and dependability with ML chatbots' flexibility and natural language processing. They are used in customer service, marketing, and other applications to quickly and accurately answer user questions and assist human operators with complicated tasks.
