Chatbots are becoming more and more popular as a way to interact with customers, users, and visitors online. But how do chatbots work and what is the technology behind them? In this article, we will explain what chatbots are, how they work, what are the different types of chatbots, and what are some of the common questions and challenges related to chatbots.
What are chatbots?
A chatbot is a software application designed to mimic human conversation in order to address customer inquiries. When a customer or a lead reaches out via any channel, such as a website, a mobile app, a social media platform, or a messaging app, the chatbot is there to welcome them and solve their problems. They can also help the customers lodge a service request, send an email, or connect to human agents if need be.
Chatbots can be used for various purposes and industries, such as customer service, sales, marketing, education, entertainment, health care, etc. Chatbots can provide 24/7 support, reduce costs, increase customer satisfaction, generate leads, and improve conversions.
How do chatbots work?
Chatbots work by using artificial intelligence (AI) and natural language processing (NLP) to understand human words or a human conversation and use pre-built solution flows that help to give a solution. The basic steps involved in how chatbots work are:
- Receiving the user input: The chatbot receives the user input in the form of text or voice. For voice input, the chatbot uses speech recognition to convert the speech into text.
- Processing the user input: The chatbot uses NLP to analyze the user input and extract the meaning, intent, and entities from it. The intent is the goal or action that the user wants to achieve. The entities are the relevant information or parameters that the user provides. For example, if the user input is “I want to book a flight to New York on Friday”, the intent is “book_flight” and the entities are “New York” and “Friday”.
- Generating the response: The chatbot uses AI and machine learning (ML) to generate an appropriate response based on the user input and the solution flow. The solution flow is a predefined sequence of steps that guides the chatbot on how to handle different intents and entities. The response can be in the form of text or voice. For voice output, the chatbot uses text-to-speech to convert the text into speech.
- Sending the response: The chatbot sends the response to the user via the same channel that the user used to communicate with the chatbot. The response can also include other elements such as images, videos, links, buttons, etc.
- Learning from feedback: The chatbot learns from feedback from the user or from human agents to improve its performance and accuracy over time. The feedback can be explicit (such as ratings or comments) or implicit (such as clicks or conversions).
What is chatbot architecture?
Chatbot architecture is the blueprint outlining its components and interactions, crucial for enabling chatbot functionality:
- User interface: This is the layer that handles the interaction between the user and the chatbot. It can be a website, a mobile app, a social media platform, a messaging app, etc.
- Channel integration: This is the layer that connects the user interface with the chatbot engine. It can use APIs or webhooks to send and receive messages between different channels.
- Chatbot engine: This is the core layer that processes the user input and generates the response. It comprises various sub-components, including:
- NLP engine: This is the component that analyzes the user input and extracts the intent and entities using NLP techniques such as tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, etc.
- Dialog manager: This is the component that manages the conversation flow and state using rules or ML models. It decides what action to take based on the intent and entities and what information to ask from the user if needed.
- Response generator: This is the component that creates an appropriate response based on the action decided by the dialog manager. It can use templates or natural language generation (NLG) techniques to generate natural-sounding responses.
- Knowledge base: This is an optional component that stores information or data that can be used by the chatbot to answer queries or provide suggestions. It can be a database, a spreadsheet, a document, etc.
- Analytics and feedback: This is an optional layer that collects and analyzes data from the chatbot interactions to measure performance metrics such as accuracy, satisfaction, engagement, conversion, etc.
- It also gathers feedback from the users or human agents to improve the chatbot over time.
What are the types of chatbots?
Chatbots can be classified into different types based on various criteria such as:
- The level of intelligence: Chatbots can be either rule-based or AI-based. Rule-based chatbots follow predefined rules and scripts to handle user queries. They can only answer simple and specific questions and cannot handle complex or ambiguous situations. AI-based chatbots use ML and NLP to understand user queries and generate responses. They can handle complex and varied questions and learn from feedback to improve over time.
- The type of conversation: Chatbots can be either open-domain or closed-domain. Open-domain chatbots can chat about any topic and have general knowledge and personality. They are usually used for entertainment or social purposes. Closed-domain chatbots can chat only about a specific topic or domain and have specialized knowledge and functionality. They are usually used for business or service purposes.
- The type of response: Chatbots can be either retrieval-based or generative-based. Retrieval-based chatbots select a response from a predefined set of responses based on the user input. They are fast and consistent but limited in variety and flexibility. Generative-based chatbots create a response from scratch based on the user input. They are more natural and flexible but prone to errors and inconsistencies.
Chatbots can be created using no-code platforms like Dialogflow, and Watson, or by coding in languages like Python and utilizing libraries such as Rasa or NLTK.
Testing a chatbot is crucial to ensure its quality and functionality, and various methods like unit testing, integration testing, and usability testing, as well as tools like Botium, Testim.io, and Chatbottest, can be employed for this purpose.
Enhancing a chatbot is a continuous effort involving data analytics for performance evaluation and user feedback for error correction and response enhancement, complemented by machine learning techniques for continual model training and updates.
Chatbots are computer programs that simulate human conversation to solve customer queries. They work by using AI and NLP to understand user input and generate responses based on solution flows. Chatbot architecture consists of various components and layers that enable the chatbot functionality. Chatbots can be classified into different types based on their level of intelligence, type of conversation, and type of response. Chatbots can be created, tested, and improved using different methods and tools.