Now that you are familiar with the concepts of candidate (C), target (T), fallback (F) and surplus (S) intents, you can use these for building better Dialogflow chatbots. Here is a formula which roughly summarizes what we have learnt till now: C = T + F + S # of candidate intents = # of […]
Now that we have defined the ideas of candidate, target and fallback intents, we can look at the next concept which will help us pull all these together – the surplus intent. A surplus intent is the opposite of the target intent – it is a candidate which you don’t want to fire at a […]
The idea behind fallback intents is mostly the same as how they are defined in Dialogflow. There are two kinds – the “catch-all” fallback intent without any input context and the “context-based” fallback intent which does have an input context. I explain more in the video below:
There will usually be many candidate intents which can fire at any given point in the conversation. But if we are building a chatbot to achieve specific goals, we would like only a set of specific intents to fire. Let us call these target intents. Clearly, target intents are a function of where you are […]
At any point in the conversation, there is a set of intents which can fire. We will call these candidate intents. To understand candidate intents, it helps to first understand which intents cannot fire. Which intent cannot fire? Suppose an intent has an input context called ContextA. Unless ContextA is active at that point in […]
There are also other issues when using followup intents. For example, it is very hard to design good fallbacks inside a followup intent tree when user provides an unexpected input. In the video below, you can see why. So what is a good way to create fallback intents in this scenario? When you use […]
In the previous lesson, you saw how you can mimic “session variable” contexts to store user inputs. What happens once we use the session-vars context? How can we read the values out? I give you a quick overview in this video: I didn’t explicitly mention it in the video, but the idea is that […]
When using the context lifespan of 1, you lose the ability to save the values you collect during the conversation. However, you can get around this issue with a little more planning and by using a “session variables” style context. I explain how in this video.
So you have understood the problem in using follow up intents. But the notion of the follow up intent is still very practical. So how can we simulate the follow up intent and get similar behavior? I explain how in the 3rd video.
So you saw in part 1 some unexpected behavior from Dialogflow. Did you spend some time thinking about what is going on? You can watch part 2 to understand what happened. Now that you understand why the default context lifespan used in the followup intents can make it difficult to understand/predict your chatbot’s behavior, […]