How to Build a Chatbot: Components & Architecture in 2025

Chatbots are communication channels that enable businesses to be available for their customers 24/7. They serve various purposes across different industries, such as answering frequently asked questions, engaging with customers, and providing deeper insights into customer needs.

Understanding chatbots’ underlying architecture is essential to reaping the most benefits. We explored how chatbots work, their components, and the steps involved in their architecture and development.

How do chatbots work?

Chatbots aim to understand users’ queries and generate a relevant response to meet their needs. Simple chatbots scan users’ input sentences for general keywords and provide an appropriate answer to the user’s query.

Chatbots can be divided into three types based on the response-generation method.

1. Rule-based chatbots

Rule-based chatbots are the earliest type. Their logic is fairly simple, and they are easy to set up and maintain.

They use “if/then” logic to generate responses selected from a predefined set of commands based on specific conditions. Although they offer limited customization options, they are dependable and less prone to generating inappropriate responses.

2. AI-based chatbots

Contemporary chatbots utilize AI, natural language processing (NLP), and machine learning (ML) to interpret users’ intentions based on the context of their messages and produce appropriate responses.

AI-based chatbots integrate NLP and ML to analyze users’ queries and recognize keywords to determine their responses. ML-integrated chatbots can self-improve through repeated interaction with users’ data, serving as new training data to expand the knowledge database and enhance the relevance and accuracy of their responses. LLM and generative AI-based systems specifically employ deep learning techniques to process natural language.

3. Hybrid chatbots

Hybrid chatbots combine rules-based systems with natural language processing (NLP) to interpret user inputs and generate responses. While it’s easier to modify their databases, these chatbots possess more limited conversational abilities compared to those powered by artificial intelligence.

Check out Types of conversational AI to learn more about what chatbot best serves your purposes.

What are the components of a chatbot?

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

Natural Language Processing

Chatbots convert users’ text and speech into organized data that machines can understand through Natural language processing (NLP). The NLP process involves several key 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 branch of NLP dedicated to interpreting the meaning of spoken language by detecting patterns in unstructured verbal input. NLU solutions are made up of three main components:

• Dictionary to determine the meaning of a word
• Parser to determine 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 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, bot training, and testing purposes. Conversations can be stored in SQL form, either on-premise or in the 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 later 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) refers to the method of converting structured data produced by machines into easily readable text for humans. Once the user’s intent is identified, NLG involves four steps to create 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 narratively.
• 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 response representation.

User interfaces

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

How to build a chatbot?

We have exemplified how to create a chatbot with basic code snippets for you. You can use these code snippets as a foundation and build upon them according to your actual needs. Remember that these architectures are incomplete and do not provide a complete user interface. You need to implement the interface and the developments yourself.

If you don’t want to build your chatbot from scratch and have a suitable budget, you can also use low-code or no-code chatbot platforms, which have many built-in features. 

1. Rule-based chatbot

To build a rule-based chatbot, you need a set of patterns and responses.

Key steps:

1. Define a set of rules or intents (patterns and associated responses).
2. Parse user input and match it against the patterns.
3. Return the corresponding response or a default fallback.

import re

# 1. Define rules: regex patterns mapped to responses
rules = {
    r"hello|hi|hey": "Hello! How can I help you today?",
    r"bye|goodbye": "Goodbye! Have a great day!",
    r"(help|support)": "Sure, I can help. What do you need assistance with?",
    r"your name": "I'm ChatBot, your virtual assistant."
}

# 2. Function to get response
def get_response(user_input):
    for pattern, response in rules.items():
        if re.search(pattern, user_input, re.IGNORECASE):
            return response
    return "Sorry, I didn't understand that."

# 3. Simple loop to chat
if __name__ == "__main__":
    print("ChatBot is running. Type 'exit' to quit.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            print("ChatBot: Bye!")
            break
        print(f"ChatBot: {get_response(user_input)}")

2. AI-Based Chatbot

You can create your chatbot with a custom transformer or a large language model API key. We explained the steps to create a chatbot with an LLM API key.

Key steps:

1. Sign up for an API key from the provider.
2. Install the SDK (e.g., openai).
3. Send user messages to the API and display the model’s response.

You can explore our guide on building chatbots with ChatGPT for a step-by-step tutorial on creating an AI-based generative chatbot: How to create your own GPT-powered chatbot?

You might also explore hybrid methods that implement rule-based filters for frequently occurring intents, while relying on AI for more open-ended questions.

What are the best practices of chatbot development?

Best practices of the chatbot development process are:

1. Identifying the target audience and understanding their needs.
   • Example: Conduct user interviews to discover common questions and preferred communication styles, ensuring the chatbot can effectively handle real user situations.
2. Setting achievable goals for chatbot implementation.
   • Example: Establish success metrics, such as reducing support tickets by 20% within three months, instead of pursuing immediate full automation.
3. Recognizing which business area will gain the most from a chatbot.
   • Example: Review customer support logs to pinpoint high-frequency inquiries that can be automated, like password resets or checking order statuses.
4. Selecting the appropriate chatbot vendor.
   • Example: Evaluate feature sets, including multi-language support, ease of integration, and pricing, through a proof-of-concept with two or three vendors.
5. Enhancing the usability and accessibility of your chatbot for customers.
   • Example: Assess the chatbot interface with users of varying abilities to ensure clear prompts, keyboard navigation, and compatibility with screen readers.

Further reading

• Conversational AI for sales
• Mortgage Chatbots
• Conversational Commerce