Optimal context lifespan in DialogFlow

Note: this is a somewhat advanced topic, and certainly a bit opinionated. I wouldn’t recommend beginners get into this article until they have built at least a toy bot and experienced all the features in Dialogflow.


If you are building bots using Dialogflow, you are probably aware of contexts. They are used to maintain state. For the specific purpose of state management, I find their implementation quite fascinating simply because they have this concept of “lifespan”.

Lifespan of a context

What is the lifespan of a context you ask? It is the number of “steps” for which a context is alive. As your user interacts with your bot, the remaining lifespan keeps ticking down by 1 per interaction until it hits zero and becomes inactive.

While the default value is set at 5, I suggest you immediately change it to the optimal value.

The optimal value

And the optimal value, in my view, is 1.

But let us first start with the problem of leaving the context lifespan at 5.

Conversations tend to meander

What I mean is, the bot asks the user a very pointed question, such as “OK, so how many red roses would you like to buy?”

To which the user says, “I am not sure. How much per rose?”. Now if you didn’t expect the user to ask this question, you now have a ticking lifespan clock and you better get the answer you want out of the user before the remaining lifespan goes to zero.

I don’t suggest designing bots with a strange constraint of trying to get the user to the right answer within a given amount of tries. The user is not playing a game of hangman with your chat bot, and the conversation will turn weird very fast if you enforce such constraints.

The machine learning is good, but not perfect

In other words, sometimes the user does give you a variant of the expected answer, but it is not recognized as a defined intent. I would propose this is actually a worse outcome than when the user actually typed in something unrelated. Why? Simply because whatever you are going to say to “recover” from this error, is very likely to further confuse the user who had typed in a perfectly reasonable answer already.

Don’t forget the first step in voice to text conversion

This is related to my previous point, of course. Remember, there is a small probability that the user’s words were incorrectly translated into text. In that case, you run into the same issue if you suggest that the user may have been on the wrong track while keeping a lifespan clock ticking.

Pre-existing domains can add unexpected complexity

I talked a little about this in my article on building a cricket stats chatbot with API.AI. The issue here is that there are pre-existing domains for which Dialogflow already populates entities. A good example is the name Steve Smith, who is a popular cricket player with a very common last name. Dialogflow identifies the Smith as a common name, but doesn’t do a good job of mapping to a user-defined entity called “Steven Smith”. It seems to think they are separate and extracts Smith out as a predefined entity and thinks the word Steve should just hang there at the end of the sentence, basically mapped to nothing.

Maybe you argue it is doing the right thing. I don’t think so, but even if it were, this is the kind of silent failure which is sure to mess up your context lifespan.

The implicit state diagram stays deterministic

Using contexts, it is possible to translate any state diagram based conversation flow into a chatbot. However, the state diagram becomes much harder to reason about if you are having lifespans greater than 1 because now you have effectively two different states that your diagram could be in at once. Again, don’t make your chat bot any harder to reason about than it already is.

Wait. So have I not made the case for higher lifespans here, given that they provide a better chance of getting back into the conversation?

This is exactly what a reader asked me recently.