How to Build a Chatbot: Components & Architecture 2026
Chatbots let businesses answer customer questions 24/7. They handle FAQs, engage customers, and collect data on customer needs.
Understanding how chatbots work helps you build better ones. Here’s the architecture, components, and development process.
How do chatbots work?
Chatbots aim to understand user queries and generate relevant responses. Simple chatbots scan input for keywords and provide pre-written answers. Advanced chatbots use AI to understand intent and generate custom responses.
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 use AI, natural language processing (NLP), and machine learning (ML) to interpret users’ intentions from 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
Converts user text and speech into structured data machines can understand.
Four key steps:
Tokenization (Lexical Analysis): Splits sentence into smaller parts called “tokens.”
- Input: “What’s your return policy?”
- Output: [“What’s”, “your”, “return”, “policy”, “?”]
Normalization (Syntactic Analysis): Fixes typos and converts to standard form.
- Input: “tmrw”
- Output: “tomorrow”
Entity Recognition: Identifies keywords to determine the conversation topic.
- Input: “I want to return my blue shirt.”
- Entities: action=return, item=shirt, color=blue
Semantic Analysis: Infers meaning by understanding word relationships and sentence structure.
- Input: “I want to return my blue shir.t”
- Meaning: User intends to return a specific item
Natural language understanding
Natural language understanding (NLU) is a branch of NLP focused on interpreting the meaning of spoken language by detecting patterns.
Three components:
Dictionary: Determines word meanings
Parser: Checks if syntax conforms to language rules
Grammar rules: Breaks down input based on sentence structure and punctuation
Purpose: Classifies user intents and generates responses based on training data.
Example:
Response generation: Pull tracking number from database
Input: “Where’s my package?”
Intent classification: order_tracking
Knowledge base
The library of information chatbot uses to respond to users.
Content varies by industry:
E-commerce chatbot: Product information, features, prices, stock availability, shipping policies
Healthcare chatbot: Physician calendars, hospital hours, pharmacy duties, appointment booking
Some chatbots integrate web scrapers to pull real-time data from online resources. Example: Travel bot scraping flight prices or hotel availability.
RAG (Retrieval Augmented Generation) combines knowledge base retrieval with LLM generation. Bot searches the knowledge base for relevant documents, passes them to the LLM, which generates a response based on the retrieved information. More accurate than pure generation, more flexible than pure retrieval.
Data storage
Stores conversation history for customer service, bot training, and testing.
Storage options:
Vector databases for semantic search (2026 standard for AI chatbots)
SQL databases (on-premise or cloud)
NoSQL for unstructured conversation data
Dialog manager
Manages conversation flow between the user and the chatbot. Tracks interactions within one conversation to adjust responses.
Example:
User: “Change my order to chocolate ice cream.”
User: “I want to order strawberry ice cream.”
Dialog manager stores: order=ice_cream, flavor=strawberry
Natural language generation
Natural language generation (NLG) is the process of converting machine-generated structured data 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:
- Define a set of rules or intents (patterns and associated responses).
- Parse user input and match it against the patterns.
- Return the corresponding response or a default fallback.
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:
- Sign up for an API key from the provider.
- Install the SDK (e.g., openai).
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
FAQ
Further reading
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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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