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