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IBM - Chatbot Series

Anatomy of a chatbot and a meaningful conversation

Empower your chatbot and add a personality Deploy your bot to slack and other social channels Continuously improve your Watson Bot using analytics and metrics (include a guest speaker from Dashbot)

I - Anatomy of a chatbot and building a meaningful conversation

Date: 10/17 Code Pattern:


Learn the fundamental concepts behind creating a chatbot that can have meaningful and natural conversations. You will learn about the Watson Assistant service and the building blocks of a conversation including intents, entities, slots, digressions, handlers and dialogs. We will also use context variables to remember our users and not get too annoying !


II - Empower your chatbot and add a personality

Date: 10/25 Code Pattern:


We built a basic chatbot in part I. We continue the journey by adding more digressions and handlers to our bot thereby giving it a personality. Additionally, we will explore the more advanced features in Watson Assistant using the JSON editor.  Finally, we will add a backend service layer built on IBM functions and IBM cloud. This enables our chatbot to queries other systems and databases to better assist the user during a conversation. We will go into the two types of communications offered in Watson Assistant - server and client.


IBM Functions

Sample Conference Bot    

III - Deploy your bot to slack and other social channels

Date: 10/31 Code Pattern:


Now that you have a functioning bot, let’s deploy it to slack ! We will explore using the built in Watson Connector as well as the botkit middleware. Additionally, we will see how some of the features of Watson Assistant surface in slack and how you can use features unique to slack to enhance your conversation. We will also briefly go over other social media channels.


IBM Watson Visual Recognition

IBM Botkit Middleware

Botkit Slack Documentation

IV - Continuously improve your Watson Bot using analytics and metrics 

Date: 11/6 Code Pattern:


Now that you have a bot live and deployed to your favorite social media, how can you continue to gather user feedback and improve your conversation ? How can the bot start to learn new terminology and content using your help ? We will look at the various metrics provided by Watson Assistant as well as adding code to our client and backend to improve our chatbot.


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