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How to Build a Chatbot: Components & Architecture in 2024

How to Build a Chatbot: Components & Architecture in 2024How to Build a Chatbot: Components & Architecture in 2024

Chatbots are a type of software that enable machines to communicate with humans in a natural, conversational manner. Chatbots have numerous uses in different industries such as answering FAQs, communicate with customers, and provide better insights about customers’ needs.

~50% of large enterprises are considering investing in chatbot development. Thus, it is important to understand the underlying architecture of chatbots in order to reap the most of their benefits.

In this article, we explore how chatbots work, their components, and the steps involved in chatbot architecture and development.

How do chatbots work?

In simple words, chatbots aim to understand users’ queries and generate a relevant response to meet their needs. Simple chatbots scan users’ input sentences for general keywords, skim through their predefined list of answers, and provide a rule-based response relevant to the user’s query. 

Modern chatbots; however, can also leverage AI and natural language processing (NLP) to recognize users’ intent from the context of their input and generate correct responses.

Chatbots can be divided into 3 types based on the response-generation method:

1. AI-based chatbots

AI-enabled chatbots rely on NLP to scan users’ queries and recognize keywords to determine the right way to respond. Additionally, some AI-based chatbots also benefit from ML integration and so can self-improve through repeated interaction with  users’ data – as new training data – in order to expand the knowledge database and improve the relevancy and accuracy of their responses.

2. Rule-based chatbots

Rule-based chatbots rely on “if/then” logic to generate responses, via picking them from command catalogue, based on predefined conditions and responses. These chatbots have limited customization capabilities but are reliable and are less likely to go off the rails when it comes to generating responses.

3. Hybrid chatbots

Hybrid chatbots rely both on rules and NLP to understand users and generate responses. These chatbots’ databases are easier to tweak but have limited conversational capabilities compared to AI-based chatbots.

To explore in detail, feel free to read our in-depth article on chatbot types.

What are the components of a chatbot?

Typically, chatbots consist of 7 components, and they are structured as follows:

Natural language processing

Natural language processing (NLP) enables chatbots to convert users’ text and speech into structured data to be understood by a machine. The NLP process consists of the following steps:

  • Tokenization: also called lexical analysis, is the process of splitting the string of words forming a sentence into smaller parts, known as “tokens,” based on its meaning and its relationship to the whole sentence.
  • Normalization: also called syntactic analysis, is the process of checking words for typos and changing them into standard form. For example, the word “tmrw” will be normalized into “tomorrow.”
  • Entity recognition: the process of looking for keywords to identify the topic of the conversation.
  • Semantic analysis: the process of inferring the meaning of a sentence by understanding the meaning of each word and its relation to the overall structure.

Natural language understanding

Natural language understanding (NLU) is a subfield of NLP which focuses on understanding the meaning of human speech by recognizing patterns in unstructured speech input. NLU solutions have 3 components:

  • Dictionary to determine the meaning of a word
  • Parser to determines if the syntax of the text conforms to the rules of the language
  • Grammar rules to break down the input based on sentence structure and punctuation

NLU enables chatbots to classify users’ intents and generate a response based on training data.

Knowledge base

A knowledge base is a library of information that the chatbot relies on to fetch the data used to respond to users. Knowledge bases differ based on business needs. For instance, the knowledge base of an e-commerce website chatbot will contain information about products, features, and prices, whereas a knowledge base of a healthcare chatbot will have information about physicians’ calendars, hospital opening hours, and pharmacy duties. 

Additionally, some chatbots are integrated with web scrapers to pull data from online resources and display it to users.

Data storage

Chatbot developers may choose to store conversations for customer service uses and bot training and testing purposes. Chatbot conversations can be stored in SQL form either on-premise or on a cloud.

Dialog manager

A dialog manager is the component responsible for the flow of the conversation between the user and the chatbot. It keeps a record of the interactions within one conversation to change its responses down the line if necessary. 

For instance, if the user says “I want to order strawberry ice cream” and then within the conversation says, “change my order to chocolate ice cream”, the dialog manager will enable the bot to detect the change from “strawberry” to “chocolate” and change the order accordingly.

Natural language generation

Natural language generation (NLG) is the process of transforming machine-produced structured data into human-readable text. After understanding users’ intent, NLG has 4 steps to generate a response:

  • Content determination: Filtering existing data in the knowledge base to choose what to include in the response.
  • Data interpretation: Understanding the patterns and answers available in the knowledge base.
  • Document planning: Structuring the answer in a narrative manner.
  • Sentence aggregation: Compiling the expressions and words for each sentence in the response.
  • Grammaticalization: Applying grammar rules such as punctuation and spell check.
  • Language implementations: Inputting the data into language templates to ensure a natural representation of the response.

User interfaces

Conversational user interfaces are the front-end of a chatbot that enable the physical representation of the conversation. They are classified into text-based or voice-based assistants. And they can be integrated into different platforms, such as Facebook Messenger, WhatsApp, Slack, Google Teams, etc.

What are the best practices of chatbot development?

Best practices of the chatbot development process are:

  • Identifying target audience and understanding their needs,
  • Setting realistic goals about chatbot implementation,
  • Understanding which business area will benefit most from a chatbot,
  • Selecting the right chatbot vendor,
  • And optimizing the usability and the accessibility of your chatbot on the customer side.

To read more about these best practices, check out our article on Top Chatbot Development Best Practices.

For more on chatbots

If you are interested in different chatbot use cases, feel free to read our in-depth articles:

And if you believe your business will benefit from a conversational AI solution, scroll down our data-driven lists of vendors:

We can also guide you through the process:

Find the Right Vendors

Links to Haptik on this page are sponsored.

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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