It’s amazing to see a well-designed Chabot at work, but it becomes even more powerful when you understand how it actually works. This is the future of technology and our future outlook. Chatbots can be seen as the most advanced shape of machine learning and the most advanced use of artificial intelligence.
1. Introduction to Chatbot
I will give you a comprehensive introduction to the chatbot creation process. From the components used to make the Chatbot to the actual flow of information in the Chatbot. Obviously, the exact components needed for a specific chatbot will depend on the type of Chatbot you want to build, but this should give you some idea of what’s available.
2. Connect Chatbot to channel
Every Chatbot needs a channel for users to interact with it. A channel is actually a messaging platform such as Facebook Messenger, Slack, Telegram, Microsoft Teams, or embedded web chat. You need at least one information platform, but you can also connect multiple information platforms and let users choose their favorite platform. Channels are the Chabot’s user interface, much like a web page allows you to interact with the software through clicks.
3. Use natural language processing
Natural language processing (NLP) engines are able to identify the intent hidden in natural language sentences. Accurately understanding “natural language” is important because, unlike low-intelligence bots that require explicit instructions, chatbots can understand human-like conversations.
For example, the following phrases all have the same intention, which is to book a flight:
- I want to book a flight
- I want to fly from Dubai to Moscow
- I need a flight
For a chatbot to understand natural language text or speech, it needs access to an NLP engine.
4. Natural language processing engines
Natural language processing engines can extract the intent behind sentences to a certain extent. Natural language processing is an area of artificial intelligence that requires certain training.
That’s why in Botpress we ask for so-called discourse. They are an important part of understanding. Discourse is a variation of a sentence, a different way of expressing the same intention. The NLP engine will take advantage of this for training.
5. Dialogue Manager/Visual Dialogue Generator
This is also the main part of the Chatbot, as this is where you design the experience. Once the Chatbot understands the user’s intent, it needs to make a decision.
There are three very common situations: taking action, asking for information, or dealing with unfounded intentions. Your visual conversation generator lets you design what to talk about, how to talk about it, and what language to talk about.
If I say, “Book me a ticket to Paris tomorrow,.” The NLP engine detects my intent to book a flight. It will extract tomorrow as the departure time and Paris as where I want to go, but the departure city is missing. The visual conversation builder will allow you to process the departure city you need to inquire about and then proceed to book your flight.
This is just a very simple example, but you can understand that in some cases, the complexity will be higher, so designing a good experience is crucial to the success of the Chatbot, which is why the conversation manager is a missing piece of the puzzle. A missing part.
6. Analysis
Analytics are needed to monitor and measure the performance of Chatbot. They provide chatbot metrics such as number of users and engagement types. For chatbot developers, collecting these metrics is obviously crucial.
It can provide you with valuable insights into user engagement, allowing you to understand possible shortcomings and key areas for improvement.
7. Content management system
Content such as user language text and media files need to be managed independently of the conversation flow. Language, media files, and execution may vary based on user identity, context, and messaging platform.
Content, like code, requires professional maintenance and source control. It allows you to separate your content from the rest of the Chatbot, making it reusable and, most importantly, easier to maintain.
Managing content independently is very simple but also very important.
8. Humans in the loop
Human-in-the-loop refers to humans being able to control the Chatbot. Some may think this isn’t a must-have feature, but it is.
There are many reasons why a human would want to take over a chatbot conversation manually, the most common being that the Chatbot doesn’t understand what the end user is saying.
We would be fooling ourselves if we believed that Chatbots are right all the time. Even as we continue to make progress in this area, we won’t be able to handle everything perfectly. When users go out of scope, it becomes critical to have people in the loop in order to maintain a good user experience.
9. Architecture
Although architecture is not a component, like any software, every Chatbot has an architecture. If the standards of software architecture are not high, the Chatbot will not be scalable and easy to maintain.
The biggest advantage of building a chatbot using a conversational AI platform is that everything is already architected for scalability and maintainability.
You only need to start a server locally or in the cloud to build and automate operations.
Botpress has a modular structure that makes it easy to activate or deactivate certain non-core component modules.
In addition to this, you can create your own modules to extend the functionality of the platform and, of course, your Chatbot.
10. How data flows in Chatbot
These elements are all key to making your Chatbot work as smoothly as possible. Take away one of these elements, and you don’t get the same experience.
11. from a macro perspective, look at how information flows.
Users enter information through channels. Messages first enter the conversation manager and are evaluated by the NLP engine. The NLP engine attempts to decrypt the sentence to find the intent and returns that information to the conversation manager.
If intent is detected, it is up to the DM to decide the next step based on the decision tree. If third-party intervention is required, the conversation manager will make the request.
All information is then sent back to the user through the same channels they have used.
While this is just a simplified version of how a chatbot works, you can certainly appreciate the complexity of building such an experience from a user experience and technical perspective.
Using an open-source conversational AI platform drastically reduces the time spent building infrastructure, ensuring you get value from your Chatbot quickly.
12. Summery
Chatbots mainly rely on natural language processing (NLP) technology to work. NLP enables computers to understand and answer user questions by analyzing and understanding human language. Specifically, chatbots typically include the following main steps:
- Natural language processing:
The Chatbot receives text information input by the user and extracts semantic and emotional information through steps such as word segmentation and syntactic analysis.
2.Information retrieval:
Based on the semantic and emotional information extracted in the previous step, the Chatbot searches for relevant information in its knowledge base or the Internet to find suitable answers.
3.Generate responses:
The Chatbot generates one or more appropriate responses based on the retrieved information.
4.Select a response:
The Chatbot selects the most appropriate response based on user feedback or a specific algorithm.
