Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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.
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.
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.
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.
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.
Typically, chatbots consist of 7 components, and they are structured as follows:
Converts user text and speech into structured data machines can understand.
Four key steps:
Tokenization (Lexical Analysis): Splits sentence into smaller parts called “tokens.”
Normalization (Syntactic Analysis): Fixes typos and converts to standard form.
Entity Recognition: Identifies keywords to determine the conversation topic.
Semantic Analysis: Infers meaning by understanding word relationships and sentence structure.
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
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.
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
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 (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:
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.
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.
To build a rule-based chatbot, you need a set of patterns and responses.
Key steps:
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:
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.
Best practices of the chatbot development process are:
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