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How to Make An Intelligent Chatbot (Pt. 2)

4 min read
intelligent chatbot

Read the first part of How to Make An Intelligent Chatbot here.


Infusing the intelligent quotient into your chatbot also depends on what you want your chatbot to do. You can either make the chatbot help the user or collect information from the user. A chatbot acting as a helper is considered to be smarter than the chatbot that serves 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. What are the characteristics that define the helper chatbot?

The helper chatbot is recognized by its natural language processing(NLP) and understanding power. Collector chatbots, in turn, leads the conversation with the user. They adhere to pre-defined question models and are not smart enough to respond when a user raises a query. The drive to increase the intelligent quotient of the collector chatbots depends on the intelligent platform where they are built to reside. How can we build intelligence into a collector chatbot?

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.


A chatbot based on the retrieval-based model works on the concept of predefined responses. The chatbot picks appropriate responses from the repository stacked 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 word go. Generative models enable longer conversations where 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 its counterpart.

Chatbot conversation framework


If you do not want to limit the conversation to a single goal or intention, then open domain proves to be the right fit. In this case, the conversation can take off in different directions and topics. In turn, 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 comes up as the best choice. Closed domain category works well for the chatbot built to achieve specific goals. Sales support system falls into this category where the topic doesn’t veer off in other directions.


Building an intelligent chatbot is not devoid of challenges. From making the chatbot context-aware to building the personality of the chatbot, there are challenges involved in making the chatbot intelligent.

Context integration

Sensible responses are the holy grail of the chatbots. Integrating context into the chatbot is the first challenge to conquer. In integrating sensible responses, both the physical context as well as linguistic context must be integrated. For incorporating linguistic context, conversations are embedded into a vector, which becomes a challenging objective to achieve. While integrating contextual data, location, time, date or details about users and other such data must be integrated with the chatbot.

AI chatbot Mitsuku

Coherent responses

Achieving coherence is another hurdle to cross. The chatbot must be powered to answer consistently to inputs that are semantically similar. For instance, an intelligent chatbot must provide the same answer to queries like ‘Where are you from’ and ‘where do you reside’. Though it looks straightforward, incorporating coherence into the model is more of a challenge. The secret is to train the chatbot to produce semantically consistent answers.

Model assessment

How is the chatbot performing?
The answer to this query lies in measuring whether the chatbot performs the task that it has been built for. But, measuring this becomes a challenge as there is reliance on human judgment. Where the chatbot is built on an open domain model, it becomes increasingly difficult to judge whether the chatbot is performing its task. There is no specific goal attached to the chatbot to do that. Moreover, researchers have found that some of the metrics used in this case cannot be compared to human judgment.

Read intention

In some cases, reading intention becomes a challenge. Take generative systems for instance. They provide generic responses for several of user inputs. The ability to produce relevant responses depends on how the chatbot is trained. Without being trained to meet specific intentions, generative systems fail to provide the diversity required to handle specific inputs.


Another factor that deserves attention is the plan to leverage NLP or machine learning for building the intelligent chatbot. In the case of natural language processing, it is about finding answers by parsing language into intent, entities, agents, actions, and contexts. With NLP reckoned as the driving force, NLP platforms like WIT, API, and LUIS can be leveraged to build an intelligent chatbot.

While you plan to leverage machine learning to create your own NLP, you must decide upon the model prior to building the intelligent chatbot. It is important to weigh generative and retrieval-based model, open and closed domains to create the intelligent chatbot that you have in mind.


Building a smart chatbot is one school of thought. Building a chatbot on an intelligent platform is altogether a different one. Today, several of successful chatbots including x.ai and Google assistant have been built on intelligent platforms. In this scenario, the platform becomes the intelligent agent, and the chatbot becomes a sensor for this intelligent agent.

The intelligent platform works to find out the goal, collect user information, process, store and convert information to realize the goal. Then the challenge is not about infusing intelligence into a chatbot but creating an intelligent platform. The focus must fall on ways to define the goal and factor sense-think-act capability into the platform.

For now, the chatbot imperative is to meet user-centric tasks. For that to happen, the chatbot must be smart and knowledgeable.  The chat to build a smart chatbot gets chattier when significant elements surrounding the building process make an entry. As we look into the future, intelligent chatbots will be built to rule the world of connections.


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