ES Quickstart Templates Overview
Sometimes users prefer to talk to a live person and not to a chatbot. This is one of the features which is not supported by Dialogflow Messenger, so we will use the Zoho SalesIQ + Dialogflow ES integration for this example.
In this article, I will explain how to use follow up intents to collect user input in Dialogflow ES. Let us take the example of a simple lead capture chatbot. It captures the details of visitors who are interested in adding a chatbot to their website.
Sometimes I see questions like these in the Dialogflow forum. ‘So, I got one intent working. How can I now “move” the conversation to the next intent?’
In the previous article I explained how you can mimic the behavior of follow up intents to move to the next intent. One disadvantage of this approach is that you cannot access the information collected from 2 or more steps prior in the current response. The way around this is to use the concept of session variables.
“When you work with Dialogflow you notice that it follows the “user types something -> agent replies with an answer” sequence. Suppose you wish to get the agent to say something before the user types anything, how do you do it?”
In this tutorial, I will explain how you can save the data you collected from the lead capture bot and save it to Airtable. Why Airtable? Airtable is the best low code database for non-programmers who are building Dialogflow chatbots
In this article, I will explain how you can use slot filling to get a list of user inputs in Dialogflow ES. Before we go further, a couple of things to note:
One of the questions people ask is the following: “After user provides some input, I would like to either confirm the input or allow them to update it” Let us use the slot filling chatbot as an example.
While the slot filling feature in Dialogflow ES makes for a very good demo, in practice it is really hard to use. One option is to use webhooks to assist your slot filling. This will give you the best of both the worlds – you allow Dialogflow ES to extract maximum possible relevant information from the user’s first sentence. At the same time, you will exit the slot filling immediately after the first sentence by using follow up events. This helps you avoid the slot filling loop and gives you more fine grained control of your conversation flow. The downside
I will explain how to use list and composite entities in the article. Suppose you sell three products – KitKat, Snickers and Twix. The user can place an order where they can ask for any quantity of each type.
A reader recently asked: “How do we create a script that will ask for a date of birth?” My immediate thought was, “Oh, that’s easy. You just ask for the date and use the built-in system entity etc”. And quite stupidly, I also acted on that thought and sent the email out. 🙁
This tutorial used to refer to inline webhooks before. But I have updated it to use a simple Python based webhook for two reasons: I don’t recommend using the fulfillment library and Python is better suited than NodeJS for Dialogflow bots.
One of the nice things about tools like Chatfuel and ManyChat is their visual interface for creating decision tree chatbots.
In this article, I will explain how you can build a Quiz bot in Dialogflow ES where the user can answer multiple choice questions (by clicking on a button) and then the bot will calculate and display their quiz score at the end of it.
It isn’t easy to use Google Sheets as the database for your Dialogflow bot. Personally, I don’t think it is a good idea to use a spreadsheet software like Google Sheets or Microsoft Excel as the database for your Dialogflow bot. A tool like Airtable is better.