-Chatbots allow organizations to interact with customers 24/7

-Personable Bots Can Respond, Even When You are Sleeping

-Bots Can Retrieve Information and Respond Quickly

Chatbots can be built through different platforms, such as via IBM Watson Assistant, or Google Dialogflow.

Dialogflow uses Natural Language Processing (NLP), which is a branch under artificial intelligence. In particular, NLP is how we can train computers to understand and process the natural human language.

Dialogflow allows you to (1) build, (2) test, (3) launch and (4) improve

You can build a chatbot all within the Dialogflow cloud platform on a web browser

Dialogflow requires minimal coding and is suitable for anyone who can effectively use a computer and web broswer

There are existing templates for building your chatbot

A Dialogflow agent is a virtual agent with the mission to handle conversations with your customers. Once a user sends a text message to the Dialogflow agent, Google uses artificial intelligence to translate the message into structured data with the aid of NLP.

Customers reach out to chatbot agent to acquire certain information. Hence, the Dialogflow agent needs to be built to handle the conversation.

A Dialogflow agent is similar to a human call centre agent. 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.

If the agent accurately identifies the user's intent, it is almost a success!

Customers reach out to chatbot agent to acquire certain information. Two different customers may ask the same thing differently, but their intent is the same. For example, if User A asks the chatbot "What time do you open on Saturday?", and User B asks "What are the opening hours on weekends?", both have the same intent of finding out the opening hours.

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.

The agent has to identify the intent, and use entities to piece together what the user is trying to say.

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.

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.

entities are facts in a sentence which refer to the who, what, where and when.

We train train our dialogflow agent to understand entities