How do chatbots work? What is the Chatbot Architecture 101? (2022)

How do chatbots work? What is the Chatbot Architecture 101? (1)

How do chatbots work? What is the Chatbot Architecture 101?

Understanding Chatbot Architecture 101: A Beginner’s Guide

The world of communication is moving away from voice calls to embrace text and images. In fact, a survey by Facebook states that more than 50% of customers prefer to buy from a business that they can contact via chat.¹ Chatting is the new socially acceptable form of interaction. It enables brands to reach out to their customers anywhere, anytime. By providing easy access to service and reducing wait time, chatbots are quickly becoming popular with brands as well as customers.

Chatbots or automated conversational programs offer more personalized ways for customers to access services using a text-based interface. The latest AI-powered chatbots can understand a query (question, command, order, etc.) from a given context by a human (or another bot, inception) and revert with an appropriate response (answer, action, etc.).

But how do chatbots work? What makes it possible for these computer programs to understand user intentions? And how are chatbots able to contextualize responses for different conversations? Let’s find out!

There can be multiple types of chatbots and the way they work changes accordingly. Chatbots can act as virtual assistants, question-answer bots or domain-specific bots. The question-answer chatbots are less complex and require a smaller skillset. They are mostly knowledge-based, and their capabilities are limited to answering only a specific set of questions. On the other hand, chatbots that harness the full potential of AI and ML can mimic human conversation and maximize user experience.

Majority of chatbots are designed to work using one of the following capabilities:

  • Pattern matcher

    (Video) How do chatbots work?

    Rule-based or scripted and structured chatbots mostly fall under this category. Such chatbots use a knowledge base which contains documents and each document comprises a particular <pattern> and <template>. When the bot receives an input that matches the <pattern>, it sends the message stored in the <template> as a response. The <pattern> can either be a phrase like “What’s your name?” or a pattern “My name is *”, where the ‘*’ is a regular expression. Typically, these <pattern> <template> pairs are manually inserted.

    This means the questions a user must ask fall in line with the programming of such QA chatbots. They classify text by segregating a piece of information (word or sentence) or generic tags from multiple categories and produce suitable responses for the end consumer.

    For instance, ELIZA, one of the early chatbots created in 1966, is a rule-based bot. It matches patterns to classify text. It parses the input text word by word, looking up for its meaning in the dictionary, ranking it based on importance, and storing it on a keyword stack. The keyword which scores the highest rank of importance is considered as the <pattern> and this pattern is then matched against the documents in the corpus to find the appropriate response. If there are no existing documents corresponding to the input, ELIZA responds using phrases like “I see” or “Please go on”.

    A simple pattern matching examples looks something like the following:

    #PATTERN MATCHING<category> <pattern>What is the capital of \*</pattern> <template> <srai>The capital of <star/></srai> </template> </category>------------------------------------------------#PRE_STORED PATTERNS<category> <pattern>The capital of the United States of America?</pattern> <template>The capital of the United States of America is Washington, D.C.</template></category><category> <pattern>The capital of India?</pattern> <template>The capital of India is New Delhi.</template></category>

    Scripted bots such as Eliza are mostly used on Messenger Platforms today. They are mostly deployed to send newsletters or daily content pieces, generate leads, do surveys, etc.

  • Suitable algorithms

    Compared to rule-based models, suitable algorithm based chatbots don’t just match a pattern against <status> or <response>. They choose a pattern matching algorithm and compare the input sentence against the <responses> in the data corpus.

    Algorithms play a major role here as they help chatbots in analyzing large datasets. This reduces the work of pattern matchers. A set of words belong to a class and with every input, each word gets counted for its occurrence. It is then counted for its common type and by leveraging algorithms, an overall rank is given to each class. The algorithms in place then provide a score to the class with the highest rank, which is most likely associated with the input sentence.

    In short, the chatbot uses the message and context of the conversation for selecting the appropriate response from a predefined list of canned messages. While the highest score only provides relativity and does not guarantee a perfect match, these chatbots provide more predictable results compared to rule-based bots.

    The availability of many algorithms has made it easier for developers to build suitable algorithm-based models. These chatbots are more practical and can be deployed to interact with users and deliver the right response. By leveraging Natural Language Understanding (NLU) and a certain set of algorithms, these chatbots can perform transactional tasks and serve specific purposes.

    These models work on the algorithms of directed flows, solving user queries in a way that it moves them gradually to a solution. Instead of intent, they follow a systematic approach and are based on pre-existing data. They are commonly used for goal-oriented tasks, keeping in mind user’s expectations from the brand and possible set of queries in a given context.

    For instance, alVin is a suitable algorithm based chatbot built with extensive knowledge of LV= Broker’s products, a UK-based provider of financial products and services. Its live chat service aids with transactional tasks and is overlapped with human support representatives, depending on complexity. alVin helps automate support process for direct queries. Human representatives are required to step in only in case of complex scenarios.

    (Video) What is a Chatbot? | How do chatbots work? | A guide to Chatbot Architecture | AI Chatbot Explained

    How do chatbots work? What is the Chatbot Architecture 101? (2)
  • Artificial neural networks

    An artificial neural network is a computing system that consists of several simple but highly interconnected elements or nodes, called ‘neurons’, which are organized in layers. These further process information using dynamic state responses to external inputs.

    The personality of generative-based chatbots is decided by seq2seq artificial neural networks. While the suitable algorithm-based models require a database of possible responses to choose from, artificial neural network-based models build responses on the fly. Instead of using canned responses, the neural network of generative models is a deep learning model dedicated to handling a set of sequences.

    The pattern detection to derive a desirable response grows stronger and accurate as the chatbot runs through each layer in the AI neural network. The three interconnected layers of the neural network which help the generative model to analyze and learn data are input layers, hidden layers, and output.

    How do chatbots work? What is the Chatbot Architecture 101? (3)

    The input layer introduces patterns into the neural network and comprises of one neuron for each component present in the input data. The neuron transmits the information to other connected neurons downstream. It is then communicated to the hidden layer, where all the processing happens through a system of connections. At the end of the process, the hidden layer leads to the output layer, which has one neuron for each possible desired output.

    A generative-based chatbot follows this neural network to understand user queries and to carry forward an intent-based communication. It conceives a sequence of context tokens (Input layer) one at a time and updates its hidden state. After processing the whole context sequence, it produces a final hidden state (Hidden layer), which incorporates the sense of context and is used for generating the answer (output layer). Thus, it combines two inputs - state and context.

    By considering the chat history or past transactions, such chatbots take into account the state. By analyzing inputs from external data points and classifying these into appropriate layers, chatbots consider the context.

    How do chatbots work? What is the Chatbot Architecture 101? (4)

    Amazon’s Alexa, Apple’s Siri, Google’s Google Assistant, and Microsoft’s Cortana are examples of generative based chatbots that are trained using many previous conversations, based upon which responses to the user are generated. AI neural networks make generative chatbots good conversational bots that employ deep machine learning techniques to resolve everyday problems in natural, conversational replies.

    While these models will always have a response, they might sound arbitrary and not make sense all the time. They run the chance of generating inconsistent replies and are prone to give answers with incorrect grammar and syntax. Admittedly, generative models can be entertaining in nature with small talk capabilities with users. But the primary focus of chatbots is to keep in mind the goal of the customer, to help resolve support queries of users, and to provide them with relevant information.

    Hence, a hybrid model with an architecture that comprises of both generative and suitable algorithm system can assist users better in scheduling reminders through a chat conversation. The flow-based suitable algorithm model has high precision and provides a grammatically accurate response but has a low recall. On the other hand, the neural conversational model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses.

    The hybrid model, by leveraging a crucial element of AI, Natural Language Processing (NLP) and combining a flow-based suitable algorithm system with a neural generative model makes the final system robust enough for a real-world application.

  • Natural Language Processing (NLP)

    (Video) How Do Chatbots Work? Simply Explained

    A hybrid chatbot at the heart of its framework has NLP, a part of AI and machine learning, which helps it to understand natural language. AI-powered chatbot decodes and processes human-understandable language within the context in which it is spoken. It understands the nuances of human conversation and realizes that commands or queries made by users do not need to be so specific.

    Chatbots, infused with NLP, simulate human-like conversation and decode user intent to generate intelligent replies. Unlike generative models, where the predefined flow makes it difficult for chatbots to have open-ended conversations, AI chatbots can engage users on a wide range of topics.

    NLP empowers chatbots to parse multiple user intents and minimizes the failures by relying on the following elements:

    Decoding intent: NLP breaks down user inputs and comprehends the meaning of the words, positioning, conjugation, plurality, and many other factors that a human conversation can have.

    Recognizing utterance: Chatbots with NLP are smart enough to recognize the instances of sentences that a user may give when they are referring to an intent.

    Dealing with Entity: Chatbots can identify entities from a field of data or words related to time, location, description, a synonym for a word, a person, a number or anything that specifies an object.

    Contextual understanding: NLP helps chatbots to decipher context by analyzing inputs like time, place, conversation history, tone, sentence structure, sentiment, etc. For instance, it is easy for the chatbot to be misled with the user reply “Great! I have to wait for another hour for my food to be delivered.” Hence, the chatbot must process positive, negative and neutral comments to analyze user sentiments effectively.

Digital interactions between brands and customers are becoming more and more complex by the day. Chatbot architectures showcase the intricacies that make conversational interfaces intelligent enough to be at par with these complex digital interactions.

As digital proliferation continues to increase, we will witness a rise in the usage of chatbots as well. Understanding what powers these chatbots will be critical in the coming years for businesses to truly unlock their potential.

Looking to harness conversational AI to automate business processes?

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(Video) What are Chatbots?
(Video) What is a Chatbot?


What is chatbot and how does it work? ›

A chatbot is a software or computer program that simulates human conversation or "chatter" through text or voice interactions. Users in both business-to-consumer (B2C) and business-to-business (B2B) environments increasingly use chatbot virtual assistants to handle simple tasks.

What is the architecture of chatbot? ›

Most chatbot architectures consist of four pillars, these are typically intents, entities, the dialog flow (State Machine), and scripts. The dialog contains the blocks or states a user navigates between. Each dialog is associated with one or more intents and or entities.

How do chatbots work an overview of the architecture of chatbots? ›

A chatbot interacts on a format similar to instant messaging. By artificially replicating the patterns of human interactions in machine learning allows computers to learn by themselves without programming natural language processing.

How chatbot works step by step? ›

Put simply, chatbots follow three simple steps: understand, act and respond. In the first step, the chatbot processes what the user sends. Then, it acts according to a series of algorithms that interpret what the user said. And finally, it picks from a series of appropriate responses.

What is chatbot in simple words? ›

At the most basic level, a chatbot is a computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person.

What is the main use of chatbot? ›

A chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.

Do Chatbots work? ›

Like all successful automation efforts, customers service chatbots can reduce costs, but the improvements they make in customer experience are far more impactful. Bots are available 24 hours a day, 7 days a week, and often answer customers' questions more quickly than human agents can.

What algorithm do chatbots use? ›

Among other things, some of the most popular algorithms used by conventional Chatbots are Naïve Bayes, Decision Trees, Support Vector Machines, Recurrent Neural Networks (RNN), Markov Chains, Long Short Term Memory (LSTM) and Natural Language Processing (NLP).

How is a chatbot made? ›

The process of building a chatbot can be divided into 2 main tasks: understanding the user's intent, and producing the correct answer. The first task involves understanding the user input. The second task may involve different approaches, depending on the type of response that the chatbot will generate. Analytics.

How a chatbot can be build? ›

To create your own chatbot:

Design your bot conversation flow by using the right nodes. Test your chatbot and collect messages to get more insights. Use data and feedback from customers to train your bot. Check which conversation routes are the most popular and improve them for a better user experience.

How do chatbots understand? ›

A chatbot is a computer program that's designed to simulate human conversation. Users communicate with these tools using a chat interface or via voice, just like they would converse with another person. Chatbots interpret the words given to them by a person and provide a pre-set answer.

How do chatbots use AI? ›

AI chatbots: How they work

A chatbot performs routine automated tasks based on specific triggers and algorithms, simulating human conversation. A bot is designed to interact with a human via a chat interface or voice messaging in a web or mobile application, the same way a user would communicate with another person.

What is a chatbot PDF? ›

In the lexicon, a chatbot is defined as “A computer program designed to simulate con- versation with human users, especially over the Internet” [3]. Chatbots are also known. as smart bots, interactive agents, digital assistants, or artificial conversation entities.

What is an example of a chatbot? ›

A chatbot helps in collecting contact information, providing available listings, and book viewings. Structurely's chatbot, Asia Holmes, is a great AI chatbot example to handle customer queries in real-time and make conversations effective.

Which is the best chatbot? ›

7 Best Chatbots (September 2022)
  • The Best Chatbots of 2022.
  • HubSpot Chatbot Builder.
  • Intercom.
  • Drift.
  • Salesforce Einstein.
  • WP-Chatbot.
  • LivePerson.
  • Genesys DX.
15 Aug 2022

What is the requirement of chatbot? ›

The most important requirements of chatbot software:

In addition to understanding and interacting within conversations, an outstanding chatbot software has NLP functions (Natural Language Processing) to analyze the context of a conversation.

Who invented chatbot? ›

The first chatbot ever was developed by MIT professor Joseph Weizenbaum in the 1960s. It was called ELIZA.

Which industry uses chatbots the most? ›

#3 Hospitality Chatbot

The real estate industry uses chatbots more frequently than any other industry—the ability for these small businesses to answer customer questions around the clock in a timely fashion is critical when it comes to making a sale, or renting a unit. It's easier than you think to implement a chatbot.

What are the technologies used in chatbot? ›

Artificial intelligence (AI), natural language processing (NLP), and machine learning are chatbot underlying technologies. They bring chatbot innovation, hence brand communication, to an entirely new personalized level.

What are the limitations of Chatbots? ›

Each of them is strictly connected with a specific limitation of this technology or its bad implementation.
  • Unclear scope of the chatbot and/or too broad purposes of its utilization. ...
  • Setting unrealistic expectations is often the reason why chatbots fail. ...
  • Lack of customer perspective in building the chatbot.
12 Oct 2021

How are Chatbots trained? ›

2. AI-based chatbots. This bot is equipped with an artificial brain, also known as artificial intelligence. It is trained using machine-learning algorithms and can understand open-ended queries.

Do chatbots use AI? ›

A chatbot is a computer program that uses artificial intelligence (AI) and natural language processing (NLP) to understand customer questions and automate responses to them, simulating human conversation.

Why chatbots are the future? ›

The future of chatbots is that they are predicted to be utilized for 47 per cent of businesses for customer support and virtual assistants, will be used by 40%, which suggests that by 2022 chatbots will help companies gain market share and become an investment in improving customer service over the next few years.

What are the characteristics of chatbots? ›

Seven characteristics of a great chatbot
  • Conversational maturity. ...
  • Omni-capable. ...
  • Integrates with CRM. ...
  • Emotionally intelligent. ...
  • Free to explore. ...
  • Autonomous reasoning. ...
  • Pre-trained.
15 Jan 2020

What type of AI is a chatbot? ›

Chatbots, also called chatterbots, is a form of artificial intelligence (AI) used in messaging apps. This tool helps add convenience for customers—they are automated programs that interact with customers like a human would and cost little to nothing to engage with.

What are smart chatbots? ›

The smart chatbot is a kind of collective name: we call “smart” all conversational agents that can accurately process the input user data, even if there is a mistake or data is distorted. The algorithms that chatbot uses for speed processing and analysis are more sophisticated in comparison to the regular chatbot.

What neural network is used for chatbots? ›

The Chatbot use Bidirectional Recurrent Neural Network (BRNN) [6].

How hard is it to code a chatbot? ›

Because building a chatbot with code is immensely difficult for people with no development background and limited exposure to coding languages, it's good to research sample chatbot code from expert developers as a jumping-off point for those determined to learn how to build their own bot without help.

How is NLP used in chatbots? ›

These AI-powered chatbots use a branch of AI called natural language processing (NLP) to provide a better user experience. Often referred to as virtual agents or intelligent virtual assistants, these NLP chatbots help human agents by taking over repetitive and time consuming communications.

Which language is used to create chatbots? ›

While making chatbots, Python makes use of a combination of Machine Learning algorithms in order to generate multiple types of responses. This feature enables developers to construct chatbots using Python that can communicate with humans and provide relevant and appropriate responses.

How long does it take to build a chatbot? ›

Implementing a chatbot takes 4 to 12 weeks, depending on the bot's scope, the time required to build your knowledge base and its technical complexity.

What are the 7 steps to create a chatbot strategy? ›

Let's walk through them in order.
  1. Audience. The first key to a successful strategy is to profile your ideal customers. ...
  2. Goal. To define the purpose or goal for your chatbot strategy, begin with the end in mind. ...
  3. Performance. ...
  4. Key Intents. ...
  5. Storytelling. ...
  6. Platform Strengths: ...
  7. Feedback.

How do you make a chatbot without coding? ›

5 Best Chatbot Platforms that Require no Coding
  1. Wotnot. Wotnot helps companies build out simple and complex chatbots for their businesses. ...
  2. Chatfuel. More than 46,000 chatbots have been created using ChatFuel. ...
  3. MobileMonkey. ...
  4. Engati. ...
  5. Botsify.

Where do we find chatbots? ›

Chatbots are on Facebook Messenger, Slack, Kik, in the App Store, and on browsers. We started a running list of chatbots that you can try, and new bots are being created every day.

Is Siri a chatbot? ›

Yes! Technologies like Siri, Alexa and Google Assistant that are ubiquitous in every household today are excellent examples of conversational AI. These conversational AI bots are more advanced than regular chatbots that are programmed with answers to certain questions.

Is Alexa a chatbot? ›

Alexa is formally a chatbot. Recently, Amazon started revealing another component on iOS that empowers clients to type their solicitations to Alexa and see reactions on the screen.

What is the difference between chatbot and AI? ›

Conversational AI is all about the tools and programming that allow a computer to mimic and carry out conversational experiences with people. A chatbot is a program that can (but doesn't always) use conversational AI. It's the program that communicates with people.

Are chatbots AI or machine learning? ›

The Types of Chatbots

Chatbots are often associated with Artificial Intelligence (AI). This happens because AI gives them the ability to handle requests without the need for human intervention. However, some chatbots don't have AI and, as such, are more basic.

Is WhatsApp a chatbot? ›

WhatsApp chatbot is an automated software powered by rules or artificial intelligence (AI) and runs on the WhatsApp platform. People communicate with WhatsApp chatbot via the chat interface, like talking to a real person. It's a set of automated replies that simulates a human conversation on WhatsApp.

What is the problem statement of chatbot? ›

Problem statement: Artificial intelligence chatbot is a technology that makes interactions between man and machines using natural language possible. From literature, we found out that in general, chatbot are functions like a typical search engine.

What is chatbot paper? ›

Chat-Bot-Kit: A web-based tool to simulate text-based interactions between humans and with computers. This ai chatbot research paper describes Chat-Bot-Kit, a web-based tool for text-based chats that we designed for research purposes in computer-mediated communication (CMC).

What is a chatbot example? ›

A chatbot helps in collecting contact information, providing available listings, and book viewings. Structurely's chatbot, Asia Holmes, is a great AI chatbot example to handle customer queries in real-time and make conversations effective.

Is WhatsApp a chatbot? ›

WhatsApp chatbot is an automated software powered by rules or artificial intelligence (AI) and runs on the WhatsApp platform. People communicate with WhatsApp chatbot via the chat interface, like talking to a real person. It's a set of automated replies that simulates a human conversation on WhatsApp.

Is Alexa a chatbot? ›

Alexa is formally a chatbot. Recently, Amazon started revealing another component on iOS that empowers clients to type their solicitations to Alexa and see reactions on the screen.

Which is the best chatbot? ›

7 Best Chatbots (September 2022)
  • The Best Chatbots of 2022.
  • HubSpot Chatbot Builder.
  • Intercom.
  • Drift.
  • Salesforce Einstein.
  • WP-Chatbot.
  • LivePerson.
  • Genesys DX.
15 Aug 2022

What technology is used in chatbot? ›

Chatbots use natural language processing and machine learning technology to turn complex business interactions into simple conversations.

What is bot communication? ›

Chatbot or bot – is a computer program that simulates a natural human conversation. Users communicate with a chatbot via the chat interface or by voice, like how they would talk to a real person. Chatbots interpret and process users' words or phrases and give an instant pre-set answer.

What is chatbot app? ›

A chatbot app is a computer program that uses text or voice commands to simulate a human conversation. It does it by sending automated messages, offering decision buttons, and synthesizing voice. Some chatbot apps use artificial intelligence (AI) to recognize the user's intent and offer appropriate replies.

Is Siri a chatbot? ›

There is an argument that the likes of Siri cannot be a chatbot because it exists outside of these channels. But this does not feel like enough of a differentiator. In fact, of more importance is the function of the chatbot (or virtual assistant) that you employ.

What was the first chatbot? ›

ELIZA was the very first chatbot as mentioned above. It was created by Joseph Weizenbaum in 1966 and it uses pattern matching and substitution methodology to simulate conversation. The program was designed in a way that it mimics human conversation.

What is the most advanced AI chatbot? ›

Mitsuku is claimed to be the most human-like conversation bot in the world. The chatbot has won Loenber price multiple times for the most human-like conversation.

What can a chatbot not do? ›

Chatbots lack proper human escalation protocols. Users like to know that they can still rely on humans whenever technology fails. However, most chatbots do not have an escalation workflow to allow a human to take over the conversation when the bot is unable to help.


1. What is a chatbot and how does it work?
2. The History of Chatbots
(IBM Technology)
3. Chatbot Tutorial: Introduction to AI Chatbots
4. Chatbots And Its Types | Learn Deep Learning
(AI Sciences)
5. Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 1
(David Shapiro)
6. What Is an AI Chatbot (a.k.a. Natural Language Processing / NLP Chatbot)?

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