Chatbox - Coggle Diagram
Agents, Intents and Entities in Dialogflow
To handle conversations with your customers
Training does not need to be overly explicit
Needs to be built and trained to reply to conversations
Categorises the customer's intention for a conversation turn
Referred to as an "end-user expression"
Matching an intent is also known as intent classification
Called a parameter or entity type
are structured data that can help us make better sense of what the user is trying to find out
How Dialogflow Works ?
1) End-user expression
2) have its intent matched
3) Response is being provided back to the user
Why use Chatbox?
Many chatbots are trained and tested to hit an accuracy of about 80% or more
Chatbot is more or less consistent in its response
For example, if you ask the E112 Engineering Design chatbot about the G101 module, it will consistently give you an incorrect response because it is not set up for that purpose
Advantages of using chatbots
Chatbots allow organisations to interact with customers 24/7
By responding back over nights
With the use of chatbots, manpower can be re-allocated to areas of work which cannot be automated
By relieving the Workload of Human workers
They help to retrieve and provide information to customers quickly
By retrieving Information and Respond Quickly
Chat conversations can be captured and used for data analysis to boost sales subsequently
can be easily Analysed
uses Natural Language Processing (NLP) under artificial intelligence
Benefits of Using Google Dialogflow
The Highly Connected World Today
customers and businesses interact beyond the usual operating hours.