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The business infrastructure is growing at flash speed. In the past decade, numerous trends and technologies have elevated the business competition so much that almost every company faces a run for its money at certain times. Technologies like AI, Big Data, 5G, IoT, and many others are key promoters of business growth.
One of the technologies that have significantly impacted the business landscape is chatbots, aka AI chatbots. Though many of you are aware of it, if not, do look for a chat window on a company website next time. The mini box on the bottom right of the window is a nudge from the chatbot.
An AI chatbot is of use for business in numerous ways, and its demand is increasing. A clear proof of that is the chatbot market. As per the stats, the chatbot market is expected to reach the $1.25 billion mark by 2025.
So, if you plan to harness the ability of an AI chatbot for your business, do read this guide carefully!
A Brief Overview of the AI Chatbot!
The word chatbot is no longer a buzzword, especially today when everyone is busy playing with ChatGPT. An AI chat is a program that leverages the power of AI and numerous other technologies and data to provide appropriate human-like responses to its users.
The chatbot aims to interpret the natural language queries from the users and generate appropriate responses in return. AI chatbots have applications in various application and domains, such as information retrieval, customer service, virtual assistants, etc.
Some of the best examples of AI-based chatbots are Slush, Cortana, Siri, etc. If we go onto some advanced chatbots, they are ChatGPT, Google Bard, Jasper, etc.
Components For Building an AI Chatbot
As easy as it may seem to give the command to a chatbot and get the desired result, the actual work is much more complex in the backend. The working of an AI chatbot has numerous technologies and components backing it. Let’s take a look at those components!
There are two primary categories among which the A chatbot components are divided!
The UI Components
Everything related to what a user sees and experiences comes in UI components. UI components include
- The user interface has all the visual components like buttons, text boxes, fields, etc.
- The user experience component includes the way things happen on the screen, like the navigation, animations, etc. Anything that improves the feel of the website.
- Conversation design is the third and the most crucial UI component. It focuses on developing the part about how the chatbot is going to interact or communicate with the users. The elements of a conversation design are flow and scripting. The flow part further includes context, entities, and intent that decide what the chatbot will say. The scripting part develops the chatbot's personality (like how the chatbot says something). In short, the conversation design includes uncovering conversation paths, responses, and fonts.
The Functional Components
The functional components behind an AI chatbot are much trickier than the UI components. Here are the functional components of the AI chatbot!
- Natural language processing is the most critical part of an AI chatbot. Generative AI and NLP help the chatbot understand user inputs, whether it is text or voice.
- Machine learning algorithms ensure that the chatbot is trained on the data. It is done via supervised and unsupervised learning.
- The knowledge base is the information repository that holds the information required by the chatbot to answer user queries. It can be product information, FAQs, etc.
- Dialogue management is another functional component of the AI chatbot. Dialogue management checks the flow of the conversation while taking care of the context, intents, and responses.
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Steps to Build the AI Chatbot From Scratch!
Ok, let’s come to the most critical part, how to build a chatbot from scratch.
1. Begin by planning the chatbot
The first step to building an AI chatbot is planning. Planning involves several things, such as defining its purpose, scope, tools to be used, conversation flow, features, etc.
2. NLP and ML
Natural Language Processing and Machine Learning are the backbones of Artificial Intelligence technology. The collaboration of both makes the chatbot fit for usage. NLP ensures that the chatbot interprets the user's requests correctly. As users tend to use slang and idioms in their natural language, NLP is trained to understand this via methods like Sentiment Analysis.
In contrast, Machine Learning is a technology that enables a chatbot to learn over time by studying and analyzing the data. With the increase in data and time, the chatbot becomes better as it can reply to users more accurately. Furthermore, the chatbot can handle complex requests. Techniques like neural networks, decision trees, and reinforcement learning can be used to implement machine learning in an AI chatbot.
3. Backend and UI
The backend of the chatbot is the part where all the functionalities reside. The backend of the chatbot is responsible for receiving the request, processing it, and generating the response. As user requests can be of various types, you have to develop programs and algorithms that interpret the user's prompts and generate appropriate responses.
This is where the programming languages like Python, frameworks like Google Dialogflow, and platforms like Chatfuel come into the picture. All of these are used to develop the backend of the chatbot. You may also integrate APIs, databases, or other systems based on the required functionality.
The other part is developing a user interface. A user interface should be visually appealing and interactive so that the user does not get bored. You are free to develop a new UI entirely or integrate with platforms like Facebook, Telegram, or Slack.
4. Integration and testing
Integration of the chatbot deals with integrating it with other systems like CRM, email marketing systems, e-commerce, etc. You can integrate our chatbot with these systems and with technologies like NLP, voice recognition, sentiment analysis, etc., to provide it with the required functionality.
After integration with all the required systems comes the testing part of the chatbot. The testing part ensures that your chatbot responses are appropriate and are not misspelled. You can create various test cases or use real-time user data to check if the chatbot provides the required and accurate responses. Tools for AI chatbot testing include TestMyBot, Botium, Zypnos, etc.
5. Deployment and monitoring
Once you are satisfied with the AI chatbot, deploy it for public use and notice its working and performance. There are various media to deploy your chatbot. You can deploy it on your servers, the cloud, or a chatbot development platform. All of these models have their set of pros and cons.
Further, you can use an analytics tool to track and analyze critical parameters like performance, user interactions, bottlenecks, etc. Based on the analysis, you can make improvements to the AI chatbot.
Why Should Your Business Build Chatbots?Find out
Chatbots have become a pivotal element of every business process today. And this has led to the advancement in numerous technologies racing to elevate the level of chatbots. The examples of ChatGPT and Google Bard are clear proof that the chatbot industry has witnessed a paradigm shift. In a scenario like this, for businesses that are still following primitive practices to serve their customers, it is time to invest in an AI chatbot.
If you are looking for the best solution leveraging artificial intelligence technology, feel free to contact us!