Two main approaches to chatbot implementation
Chatbots can vary in their complexity: simple bots provide short answers to set questions, while advanced chatbots can hold a conversation with unpredictable and complex queries. This difference between rule-based and AI chatbots lies in their complexity. It determines the chatbot’s architecture and technology stack.
Let’s start by exploring types of chatbots based on their technical logic and architecture.
AI-based chatbots
AI-based chatbots, also known as conversational AI chatbots, are complex programs that imitate a natural human conversation and can recognize queries regardless of their wording. Such bots can be based on a wide range of AI algorithms that allow them to comprehend human speech, learn during the conversation, and come up with relevant answers based on vast datasets.
Depending on the type of interaction, AI-based chatbots can be divided into two groups:
- Text chatbots
- Voice chatbots
Some chatbots, such as Google Assistant, Amazon Alexa, and Siri, combine these two types, allowing both text and voice input.
One of the most prominent examples of conversational AI today is ChatGPT from Open AI. It is able to recognize natural human language and provide relevant, comprehensive answers, perform calculations, write code, and assist users with almost any request. ChatGPT is applicable to many industries, including healthcare, education, tourism, e-commerce, and finance.
At Apriorit, we use various approaches to human language processing to build AI-based software, including:
- Natural language processing (NLP)
- Machine learning (ML)
- Natural language generation (NLG)
- Automatic speech recognition (ASR)
- Dialog management (DM)
- Deep learning (DL)
The main advantages of AI-based chatbots are:
- Enhanced and personalized customer experience
- Self-learning and adaptation
- Ability to handle complex queries and unlimited interaction scenarios without the need for manual updates
- Relevant, human-like answers
Among the disadvantages of AI-based chatbots are:
- Cost of implementation
- Technical complexity
- Privacy concerns
- Need for constant monitoring and fine-tuning
AI chatbots offer businesses a competitive edge by providing highly personalized interactions and valuable data insights. Despite the need for initial investment and technical challenges, their ability to enhance the customer experience, scale operations, and drive revenue makes them great for business growth.
Rule-based chatbots
Rule-based chatbots are simpler than AI-based chatbots, as they use predefined scripts to answer particular questions. Rule-based chatbots can’t comprehend a natural conversation, but they can follow a rule-based matrix to guide users to a specific action or information.
What is a rule-based chatbot in terms of communication methods? Unlike AI-based chatbots, customers can communicate with rule-based chatbots only via text. Rule-based chatbots don’t support voice recognition, as that requires advanced technologies like AI and ML.
There are two categories of rule-based chatbots:
- Button or menu-based chatbots offer users a list of predefined questions to choose from and provide corresponding answers. This puts strict limits on a conversation, so these chatbots are often used for the most basic customer support.
- Keyword recognition-based chatbots allow users to write their own questions and recognize keywords within those questions that trigger corresponding answers. Keyword recognition chatbots can provide irrelevant answers if keywords are phrased differently from what’s predefined in the database.
Let’s take a look at the pros and cons of rule-based chatbots.
The main advantages of rule-based chatbots include:
- Ease of implementation and integration
- Low cost of implementation
- Tight control over data, and, consequently, more security
- Ability to handle generic questions and take the load off human support
Among the disadvantages of rule-based chatbots are:
- Inability to learn on their own
- Linear conversations and limited requests
- More frequent irrelevant answers to customers’ questions
- Only manual updates and improvements or additions to current scenarios
Overall, rule-based chatbots are relatively simpler and more cost-effective than AI-based chatbots, allowing businesses to provide basic support, answer frequently asked questions, share news, or collect contact information without any human participation.
Let’s now compare AI and rule-based chatbots side by side to help you determine which is best for you.
If you can’t choose between these two options, you can consider a hybrid chatbot that combines elements of both types. Let’s talk about hybrid chatbots in detail.
Hybrid chatbots
Hybrid chatbots are a combination of rule-based chatbots and AI-powered chatbots. They leverage the strengths of both approaches to create a more versatile and efficient conversational experience.
Hybrid bots cover both generic queries and complex issues in one place: the rule-based component handles routine inquiries like order status checks, return policies, and frequently asked questions. Meanwhile, an AI component understands complex and context-based queries, learns during the conversation, and references past interactions to provide the most relevant answers.
An example of such a chatbot is Amtrak’s Julie Virtual Assistant — a hybrid chatbot from a national railway service in the US. Its rule-based scripts handle common questions on ticket reservations, refunds, train schedules, and so on. In addition, it uses AI and NLP to answer complex queries about matters such as multi-city itineraries.
