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Lesson 10 - chatbots - Coggle Diagram
Lesson 10 - chatbots
why use chatbot
-personable bots can respond, even when you are sleeping
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Agents, Intents and Entities in Dialogflow
agents
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Whether it is a Dialogflow agent or a human agent, both need to be trained to handle conversational scenarios. Training does not need to be overly explicit.
intent
An intent categorises the customer's intention for a conversation turn. You can define mutiple intents for a single agent, just like how a call centre agent can decide if you need basic troubleshooting, make an appointment, or redirect your phone calls based on what you say.
What a customer types to the chatbot is referred to as an "end-user expression". Dialogflow matches the end-user expression to the best intent in your agent. Matching an intent is also known as intent classification.
entity
When an intent is matched, Dialogflow can extract specific information from the end-user expression. The extracted data is called a parameter or entity type. Parameters and entity types are structured data that can help us make better sense of what the user is trying to find out.
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Test this chatbot
In an increasingly connected world, customers and businesses interact beyond the usual operating hours. In the past, a consumer would visit a mall, take a queue number, and seek help at the customer service counter. There are additional platforms which allows the mall to interact with its clients autonomously.
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follow-up intents
This is because real life conversations are more than a simple question-and-answer. After our chatbot provides an initial response to a customer's question, we can expect the customer to follow-up from that response.
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