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How to Make An Intelligent Chatbot (Pt. 1)

4 min read
intelligent chatbot

The power of chatbots is not lost on the business world. As brands focus on promoting personalized experiences, more and more intelligent chatbots are being built to engage users and improve brand image. That said it is a rarity to find a live intelligent chatbot, also called as AI chatbot. As the thought of a chatbot springs up, we know it is not a real person for sure. What we know is that chatbot brings a human touch. For that to become a reality, chatbots need to be really intelligent. The crux is not the chatbot rather it is the intelligence quotient of the chatbot that can bring the human touch.

It is the intelligence that gives power to the AI chatbot to learn from conversations and handle any and every situation that comes its way. As chatbots move into complex territories, raising the intelligence quotient becomes increasingly difficult. How to build smart chatbots and what deserves our attention?


A chatbot is smart when it becomes aware of user needs. For instance, let us consider the case of a live chatbot helping a user book a room in a hotel. The user is prompted to give out the date the user has in mind to book the room. So far so good until the query ‘Are premium rooms available’ comes from the user. Now the AI chatbot must understand this specific user need to provide a relevant answer. An intelligent chatbot will understand and learn the language nuances to give a convincing answer. In the future, there will come a time where the bots will have artificial intelligence which will know what we want before we even ask it.

To cut down design complexity, it is important to ignore proactive user queries by keeping it local. But an AI chat bot is based on the human capability of self-learning and gaining information efficiently. Thus it’s imperative to make the chatbot sense natural language utterances. There are tools like IBM Watson, Api.ai, and Wit.ai to incorporate natural language capability into a chat bot.


If a chatbot is smart, then learning becomes a distinguishing trait of the chatbot. An intelligent chatbot is one that learns conversations all the time to improve its performance. The modules in a chatbot including user modeling modules and the natural language understanding module which can perform better by learning continuously. Machine learning(ML) algorithms and human supervisors enable the learning of the chatbot. ML techniques like reinforcement learning supervised, and unsupervised techniques can be leveraged to ensure the AI chatbot becomes a good learner.

The ability to learn is a key factor in creating an intelligent chatbot. With neural networks and deep learning, chatbots can become good learners. Learning is paramount to ensure that the chatbot recognizes patterns in data it receives and responds to user requests in the most appropriate way.


A chatbot is primarily built to serve the user request. It is crucial for the chatbot to plan how to perform the task requested by a user. Chatbot responds to each user request by learning from the conversation so as to what the request is. Progress from one user request to another also requires planning until completion of the task. When it comes to complex tasks, chatbots must identify the action sequence to do the primary goal of the user. Planning is a sequence of actions which form conversations and include acknowledgment, questions, and information. As it learns from conversations with the users it will continue growing smarter and smarter with each conversation.


The AI chatbot comes with the ability to fix a goal and work autonomously to achieve that goal. This is easier said than done where identifying the goal for a specific situation is a hurdle in itself to cross. The chatbot adheres to a three-step process for realizing the goal. It is the sense-think-act cycle that can define the intelligence of a chatbot. An AI chatbot goes through this cycle to make progress towards pre-defined goals autonomously.

Sense-think-act intelligent chatbot

Ability to sense

For an AI chatbot, sensing the environment where it resides becomes a prerequisite for getting the information required to perform a task. The chatbot finds it easy to listen to what the user says than make sense of what is being conveyed by the user. Take the case of a robot that you want to build. It becomes a challenge to infuse sensing power into the robot for there is a dire need to integrate the robot with most modern sensors.

Sharp to think

In simple terms, chatbot must think what to do when a user places his request. The chatbot must convert information received from a user into an understandable format and store it in a knowledge base. An AI chatbot makes a decision by leveraging pre-existing knowledge and one that it acquires continuously. Based on this decision, the chatbot takes action to achieve pre-defined goals.  Use neural networks in machine learning to make the chatbot think and take actions depending on the request placed by the user.

The knowledge base influences the learning capability of the chatbot from its past conversations with users. Take the case of Siri and Google Now. Their intelligence is due to the knowledge stored internally. This knowledge base helps in learning faster, identifying relevant information and providing a response that is relevant.

Information gathered and learned guides the chatbot to decide on the relevant action. Taking decision is more about what the chatbot has to reply to a user’s request. Predictive analytics using machine learning can make the AI chatbot plan ahead about queries that would come from the user. This can make the chatbot more intelligent.

Quick to act

As the thought cycle gets over, the chatbot knows the action it has to take to respond to a user. Now, the chatbot has to act.  The chatbot must now type out the reply to a specific query raised by the user. Typing out a sentence is relatively easy for a chatbot when compared to responding via its audio or video capabilities. For audio or a video chatbot, responding to the user through a suitable action becomes difficult in the way it has to sound like a human.

Read part 2 of How To Make an Intelligent Chatbot here.

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