Building a high-performing Dialogflow agent, part II

Sat, 05 Mar 2022 00:00:00 +0000

After a first part on the general principles of Dialogflow and on building a high-performing dataset, we’re going to apply all of it to building a conversational agent!

We’ll focus on creating the dataset, and on the traps to avoid. This article, on the other hand, will skip Dialogflow’s integration features, such as context, webhooks, and the nocode Dialogflow CX feature that lets you build and integrate a complete agent without a line of code (well, almost). But don’t panic (the word is very well chosen). We’ll get to that another time!

Building a high-performing Dialogflow agent, part I

Tue, 30 Nov 2021 00:00:00 +0000

In the chatbot world, two approaches clash, with a strong impact on user experience: NLU, or not?

NLU can create a unique conversational experience for users. Implemented badly, it can just as easily produce a truly disappointing one.

At Coddity we specialize in Google’s nocode chatbot platform, Dialogflow, and around it we’re going to walk you through two blog posts:

How do you build and optimize an NLU implemented on Dialogflow? (the subject of this part I)
How do you create a dataset for a fictional bot? (part II, coming up)

NLU?

NLU, for Natural Language Understanding, makes it possible to understand a user’s intent automatically from the sentences they type. This method is one branch of research in AI / data science.

Data on NotNil Blog

Building a high-performing Dialogflow agent, part II

Building a high-performing Dialogflow agent, part I

NLU?