Chatbot architecture is an important component in the development of a chatbot. It is based on the use case of business operations. Clients personalize chatbot architecture to their preference in order to maximize its benefits for their specific use cases.
1. What is Chatbot architecture?
Chatbot architecture is a key component in the development of a chatbot. It is customized based on the usability and context of the business operations. Key elements of chatbot architecture are dependent on the requirement of the client.
2. How does a chatbot work?
A chatbot is a software service that is created to mimic human language. Clients can interact with these tools by using a voice or text-based interface in a similar way to how they would interact with another individual. A predefined answer is given by the chatbot based on the interpretation of the words given to them by a user.
3. What are the components of a conversational chatbot architecture?
- Question and Answer System
- Plugins/Components
- Node Server / Traffic Server
- Front-end Systems
4. What's an NLP Engine?
NLP Engine consists of 2 components:
- Intent Classifier: Intent classifier is used to analyze inputs that are given by the user and identify it with the set of intents that are supported by the chatbot.
- Entity Extractor: The duty of the Entity extractor is to extract vital data from the user’s question/query.
5. What is Agent for Dialogue Management in chatbot architecture?
- Feedback Mechanism: Here the agent takes feedback from the user from time to time to ensure that the bot is doing fine with the conversation and also that the user is satisfied with the bot’s response. This reinforces the bot to find out its mistakes and corrects itself in future conversations.
- Policy Learning: Policy learning could be a higher-level framework that teaches the bot to require more of happy paths during the conversation in order to enhance the overall end-user satisfaction. It is used for creating a network of effective routes that directly lead to higher user satisfaction scores. The bot then tries to find these interactions and follows the interaction flow about the conversation it has had with any similar users in the past.
6. How is questions and answer training done in chatbot architecture?
- Manual Training: The process in which an expert creates FAQs (Frequently asked questions) and then maps it with relevant answers is known as manual training. This helps the bot identify important questions and answer them effectively.
- Automated Training: Automated training involves submitting the company’s documents like policy documents and other Q&A style of documents to the bot and ask it to the coach itself. The engine comes up with a listing of questions and answers from these documents. The bot can then answer these questions confidently.
7. What are Plugins/Components of chatbot architecture?
Plugins/Components give chatbots the ability to have APIs and smart automation components that can be used for various internal company uses like HR management systems as well as field-work related conversational UI.
8. What's a Node Server / Traffic Server in chatbot architecture?
The node/traffic server handles data traffic from end-users and effectively routes them to the relevant components. The routing of internal components to front-end systems is also done by the node/traffic server.
9. What are Front-End Systems in chatbot architecture?
Front-end systems are often any client-facing platforms. They will be the particular chatbot interfaces that reside in various platforms like:
- Facebook
- Slack
- Google Hangouts
- Skype for Business
10. What is your chatbot designed for?
A chatbot is a computer software tool that is created to simulate human conversation online. It has the ability to act like an assistant that can interact with its users through text messages and also be an effective website and app integration that helps improve customer engagement.
11. How to build a chatbot?
You can build a chatbot for your business with the following core components.
- FAQs – Each FAQ represents a collection of query variations and an expected response for those. This is often the building block for you to train the chatbot for NLP intelligence.
- Paths – With the help of user-friendly drag and drop UI, Engati has one of the best conversation flow modelers available to create a conversation flow.
- Intents and Entities – For advanced intelligence, you'll create FAQs with intents that handle system entities like dates, locations, etc moreover as custom entities.
- To aid with the conversation - Intelligence, capabilities to handle synonyms, stopwords, etc. are available for you to configure and manage.
Besides the core build components, there are a variety of features available to assist you along with your marketing, customer support, HR, and repair management requirements. The key ones here include:
- Live Chat: During an interaction, if your bot is not able to solve the query then Engati live chat has the capability to transfer the call to any live agent.
- Broadcast: In order to keep your user engagement high you can use the push messaging feature via your bot.
- Integrations: Engati has the capability to integrate with a large variety of internal apps and even cloud interfaces.
- Analytics: Identify, understand, and analyze a large number of interactions with your chatbot in order to gain customer insights. As you study the individual interactions of your users you will be able to improve your chatbot.
12. What classic algorithm is commonly used in a chatbot?
The classic algorithm that is commonly used in chatbot use cases is the Multinomial Naive Bayes algorithm. It focuses majorly on NLP and classification of text.
13. What are three common chatbot design issues?
- Accepting user failure or corrections. A typical misconception within the conversation design is to fail at recognizing user corrections.
- Dealing with slow user response. Some users aren’t as quick in typing as your chatbot is.
- Jumping between conversational threads.
14. What's multi-chatbot architecture?
The answer to tackling single bot issues is to style your solutions with a multi-bot architecture in mind. Think about bots in a very similar way as to how you think about employees in an organization i.e. where each serves a different role and skillset. No single person in the company has the ability to undertake all aspects of the business.
15. What's chatbot backend architecture?
Chatbot architecture is just like the architecture of an online application. It works on the client-server model. The key difference between a traditional backend structure and a chatbot backend architecture is its capability to gain insights with unstructured data.
16. What are the components of NLU (Natural Language Understanding)
NLU has 3 specific concepts, namely:
- Entity: A custom data point that can be extracted from the query or conversation is known as an entity.
- Intents: Intent is known as the predefined action a chatbot is going to do when asked a query.
- Context: With context, you can easily relate intents with no need to know what was the previous question.
17. What are the steps of NLP (Natural Language Processing)?
The steps taken to convert user query into structured data that can further be used by your chatbot to answer these queries are as follows:
- Sentiment Analysis.
- Tokenization.
- Named Entity Recognition.
- Normalization.
- Dependency Parsing.
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