How to build an intelligent chatbot?

YEREVAN, Armenia - March 17, 2022 - PRLog -- Building a chatbot may seem like a complex process. But if you believe that your users will benefit from it, you should definitely give it a try!

Using artificial intelligence and natural language processing, a chatbot can simulate conversation with users through messaging applications, websites, and mobile apps, giving them accurate and relevant information.

Let's go through all the necessary steps for building a chatbot.

Define the Goal

Before you start building your chatbot, you need to clearly understand what customer problem you're solving with your chatbot, what your bot is going to do exactly, and what it will do for the user. You can build a rule-based chatbot with predefined answers or an advanced AI-enabled bot that keeps learning from user input.

Types of Chatbots: Rule-Based Chatbots vs AI Chatbots

As the name suggests, rule-based chatbots use a series of defined rules. Such types of chatbots are used to answer questions that are often simple.

An AI chatbot is powered by Natural Language Processing (NLP). AI chatbots use machine learning to understand the context and intent of a question before formulating a response. The more you use and train these bots, the more they learn and the better they operate.

Select a Channel

You can use chatbots across many channels, but it's preferable to use the same chatbot stack across all the platforms

Choose the Tech Stack

You can build a chatbot app with the following bot constructors' help:

• Chatfuel
• Flow XO
• HubSpot
• QnA Maker
• Botsify

Frameworks for chatbot development

• Wit.AI
• IBM Watson
• BotKit
• Microsoft Bot Framework

Design the Conversation

Once you've selected a tech stack, you can finally build your chatbot by designing the conversation flow. If you have chosen DIY platforms, you can build the conversation flow by dragging and dropping building blocks, so they create a sequence.

Train the Bot

An essential part of AI-powered chatbot development is training the bot with real-life data. You can either train your bot on some existing data sets (such as emails, support tickets) or get a third-party data set with the information that your bot needs to know.

Testing and Deployment

Now it's time to test if the chatbot meets customers' needs. In fact, the development life cycle doesn't end here. Now you need to collect feedback from users to improve it.

For more information about how to build a chatbot, see the following blog post:

How to Make a Chatbot: Best Practices, Technologies & Business Benefit [Guide] (https://addevice.io/blog/how-to-make-a-chatbot-from-scratch/)