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How to teach your chatbot to recommend rather than give a generic answer | by Rensen Ho | Jan, 2022

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How to teach your chatbot to recommend rather than give a generic answer | by Rensen Ho | Jan, 2022

Source: https://leapdesign.com/upsideup/2010/04/28/decision-tree/Have you asked a specific question to a chatbot, only to get a “read more” link to another page? For example, whether you ask a broad question like “is the stadium open?” or a specific question like “are the indoor courts open despite the rain?”, the bot links you to a lengthy page for more information about the venue operating hours and conditions.If you have grown your FAQ bot to serve thousands of unique questions, that’s a fantastic accomplishment! However, if every intent is designed to return one kind of response, then that single response will serve a bucket of questions at various specificity. There are more details provided in the question, but the bot fails to capture and utilise the entities for decision making. In...

Why good chatbots need context, not tree-based flows | by Rensen Ho | Sep, 2021

In the example, you’re interested in visiting an attraction site and want to find out how much the entrance tickets are, so you ask,User: “How much are tickets for 2 adults and 1 child to the cloud forest?”Surprisingly, the chatbot didn’t know the answer, despite having the relevant API integrations.Bot: “Sorry, I’m still learning.”With a bit of guidance, the chatbot redirects you to a guided (rule-based) conversation flow. It suggests that you should say “Buy tickets” first, followed by “Ticket prices”, and finally “Cloud Forest” to get to the answer.Bot: “Tickets are available on the website.”Not quite close yet.The vast majority of virtual agents use a natural language understanding (NLU) model, but users are still stunted with the unnatural dialogues.One cannot simply explain the intel...

Making chatbots reply smarter with context using Dialogflow Fulfillment | by Rensen Ho | Sep, 2021

1. Instead of one intent with the required slot filling parameters, create that intent followed by one intent for each parameter. (See purple boxes above)2. In those intents with a single slot filling parameter, set it optional.3. Put all entities extracted from any intent into the conversation context programmatically. (See the blue box above)4. Make a functional response for a group of related intents (see the orange box above), so that you’re making a chatbot to reply based on the user’s intent and information (either mentioned or referred from context), instead of intent without information.Let’s take a closer look at the code.Start by creating a map of intents. Let the agent (a webhook client) use the intent map to handle incoming messages.Remember to create those intents in Dialogflo...