Understanding Architecture Models of Chatbot and Response Generation Mechanisms

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Understanding Architecture Models of Chatbot and Response Generation Mechanisms

Analyze chatbots, one of the most popular, widely adopted, and accessible ways to utilize artificial intelligence in real life.

· AI Zone ·
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This is the generation of chatbots and artificial intelligence. The recent advancements in this arena are sprouting to the extent that chatbots are replacing humans in customer service. Artificial intelligence has grown to become something more than a mere science fiction dream. Did you ever think that humans would be interacting and communicating with intelligent machines? Chatbots have made this unrealistic thought possible with its intelligence, human-like replies, and ability to learn with experience through machine learning.

Chatbots are one of the most popular, widely adopted, and accessible ways to utilize AI in real life. Businesses are looking at chatbots as an important tool for automating customer service and business processes.

Different Types of Architecture Models of Chatbots

The architecture model of a chatbot is decided based on the core purpose of development. There are two types of possible responses of chatbot: it can either generate a response from scratch as per machine learning models or use some heuristic to select an appropriate response from a library of predefined responses.

Generative Models

This model is used for the development of smart bots that are quite advanced in nature. This type of chatbot is very rarely used, as it requires the implementation of complex algorithms.


Generative models are comparatively difficult to build and develop. Training of this type of bot requires investing a lot of time and effort by giving millions of examples. This is how the deep learning model can engage in conversation. However, still, you cannot be sure what responses the model will generate.

Retrieval-Based Models

This architectural model of a chatbot is easier to build and much more reliable. Though there cannot be 100% accuracy of responses, you can know the possible types of responses and ensure that no inappropriate or incorrect response is delivered by the chatbot.


Retrieval-based models are more in use at the moment. Several algorithms and APIs are readily available for developers to build chatbots on this architectural model. This bot considers the message and context of the conversation to deliver the best response from a predefined list of messages.

Response Generation Mechanism of Chatbots

Let’s discuss two different ways by which chatbots understand user’s message or get its intent.

Pattern-Based Heuristics

A response can be generated by two different ways: using if-else conditional logic or using machine learning classifiers. The simplest way out is to define a set of rules with predefined patterns that act as the condition for the framed rules.

Artificial Intelligence Markup Language (AIML) is most popularly used for writing patterns and response in the process of chatbot development.


<pattern>What is your name</pattern>

<template>My name is Albert Smith</template>


With a natural language processing pipeline and predefined rich pattern, AIML can be used to build a smart chatbot. These bots parse user message, find synonyms and concepts, tag parts of speech and find out which rule matches the user query. However, these bots do not run machine learning algorithms or any other APIs unless specially programmed.

Intent Classification Using Machine Learning

Though pattern-based heuristics deliver good results, the problem is that it requires all the patterns to be programmed manually. This is a tedious task, especially if the chatbot has to distinguish hundreds of intents for different scenarios.

Intent classification is completely based on machine learning technology that allows for the training of bots. With a training set of thousands of examples that are most likely to be faced by the chatbot, it can be trained to pick up patterns of data and learn from it.

scikit-learn is a popular machine learning library that helps in executing machine learning algorithms. Developers even have the option to use one of cloud APIs among api.ai, wit.ai, and Microsoft LUIS. Recently bought by Facebook, wit.ai was the first machine learning API for chatbots.

Response Generation

Once the chatbot understands the user’s message, the next step is to generate a response. One way is to generate a simple static response. Another way is to get a template based on intent and put in some variables. The chatbot development company chooses the method for generating the response depending on the purpose for which chatbots are employed.

For example, a weather forecast chatbot that uses API to get a weather forecast for the given location can either say, "it will most probably rain today" or "it’s a rainy day" or "probability of rain is 80%, so put your umbrellas to use today."

The style of response varies from user to user. In that case, the bot can study and analyze previous chats and its associated metrics to tailor customized responses for the user. The following diagram is the representation of separate response generation and response selection modules:


Building a chatbot from scratch that perfectly serves your purpose requires professional help. It is recommended to procure chatbot development services from a trusted company that has good experience in building chatbots that give human-like responses.

Images in this blog are taken from pavel.surmenok.

ai ,architecture ,bot development ,chatbots ,classification ,machine learning

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