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1. Rule-Based Chatbot
As the name suggests, these chatbots use a series of defined rules. With this type of bots, the communication is through pre-defined rules and a set of questions.
These chatbots are not able to generate their own answers but with an extensive set of answers and smartly designed rules, they can be proved very useful and productive
These chatbots are also referred to as decision-tree bots and the reason is that rule-based chatbots are guided by a decision tree, the customer or the user is given a set of predefined options that lead to the desired answers.
One limitation of these chatbots is that they don’t answer any question outside of the defined rules. But it can’t be said a drawback as the main mechanism of these chatbots is to answer questions bounded by the defined rules.
Where it is used:
Rule-Based chatbots are used in a scenario where a customer or user wants to perform a certain task, in that case, to automate that process rule-based chatbots are preferred over AI-based chatbots.
Those tasks may be such as booking a flight ticket, booking a movie ticket, inquire about the flight timing and many more. Most companies are using rule-based chatbots to increase their customer satisfaction and answer their queries.
Rule-based chatbots are used as an FAQ resource and they don’t need to have a ton of example conversations to feed it for the response.
- These chatbots are highly secure and accountable
- Faster to train and less expensive
- Faster implementation of bots
- It can be easily integrated with legacy systems
- The interactions with chatbot feel robotic rather than conversational.
- They can not learn on their own, so we need to train and improve them manually.
- Can not operate on a standalone basis.
2. AI Chatbot
AI-based chatbots are built using complex Machine Learning models that enable them to self-learn with the help of provided data and then generate the answers accordingly.
In AI-based chatbots, we train the chatbots using Machine Learning models so that they are able to make connections between questions that are asked by users in different ways and languages.
These chatbots are able to understand the context and intent of a question before answering them. Once the intent of the question is clear, they generate their own answers to more complicated questions with the help of natural language processing.
Natural Language Processing(NLP) is the key that makes the AI chatbots understand and respond to humans questions. They use machine learning and the capabilities of AI that makes bots smart with time.
Where it is used:
AI-based chatbots are used where you need to simulate the chats the same as human behaviour. There are many tasks that are being performed by AI bots such as hiring a cab online, ordering food online, checking the weather reports etc.
Using these AI-based chatbots you can divert precious human resources to some more important tasks. Human intervention is required only when a very complicated query is asked by the user.
Some best examples of AI-based chatbots are Alexa, Siri and Google assistant.
- Learn from the provided information
- Good at engaging with customers
- Can understand and interact with many languages
- Understands intent and context of the question
- Can not be trained with fewer data, needs more data to be trained.
- It takes too much data to be trained, which makes the implementation process longer and complex.
- If the chatbot has learnt something wrong, it will take enough time to be corrected.
Which one is better?
As we have seen, AI-based chatbot and Rule-based chatbot both have their own advantages and limitations. Therefore, it’s better to go for a hybrid chatbot. Hybrid chatbots have some rule-based tasks, and they also understand human intent and context.
This makes a hybrid chatbot a better and balanced tool for businesses to interact with their users and customers.
One great example of a hybrid chatbot is the Medical Diagnosis chatbot. These chatbots ask the patients about their symptoms in a rule-based pattern, and the AI-based chatbot pattern can be used to solve their queries.
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