15 things all Dialogflow bot makers should know
Here are a list of things that I would like to tell anyone who is getting started with Dialogflow.
Things all Dialogflow bot makers should know
Note: These are not in any particular order 🙂
NLU powered chatbots are different from scripted bots. This means a Chatfuel bot can never do what a Dialogflow bot can.
Regex entities have already been added into Dialogflow. Please start using them and don’t reinvent the wheel.
You cannot really have Dialogflow initiate the conversation. Even in those cases it seems possible, something much more complex is going on under the hood.
Dialogflow has a 1-click integration which lets you add a chatbot to your website.
Unfortunately, the 1-click integration is also very limited, and you need to create your own integration if you need any of these things: clickable hyperlinks with anchor text, images, buttons, bold face, other text formatting, and any other type of advanced control such as list boxes.
Slot filling is a very tricky feature to implement and I recommend avoiding it
The lifespan you choose for your contexts will decide your candidate intents at each step in the conversation, so I recommend using a lifespan of 1
You can jump into agent creation without creating a flowchart first, but you will find that your agent becomes hard to manage very quickly.
Agent ZIP file
Dialogflow provides an agent ZIP file import/restore feature. This means you can define all your intent training phrases in a spreadsheet, create a tool which converts it into the ZIP file format, and import it, and you will have a working Dialogflow agent
Export agent ZIP file
You can also EXPORT the agent ZIP file. This means, instead of wasting time clicking into each and every single intent to find a specific training phrase or response, you can create a tool which lets you search across your intents
You can break your Dialogflow agent just by doing some innocuous thing, like “approving” a phrase in your Training tab
Dialogflow is not open source, and it is possible they might never release the source code as open source.
Almost no one (outside of the Dialogflow team) knows what machine learning algorithm is used in Dialogflow
(But there is an article which came out recently which might provide some hints)
You don’t need to write code just to understand how webhooks work under the hood
The wildcard entity should be used carefully and sparingly
Learning a little bit of NLU (Natural Language Understanding) can be very helpful when it comes to building Dialogflow agents.