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Detecting dead ends in the Chatbot Conversation Flow | by Nikolett Torok | Apr, 2021

Ready, steady…GO!

Rule-based chatbots are a good starting point to gain experience in designing chatbots. These decision-tree based bots are following a set of rules and use a planned, guided dialog.

Behind the apparent simplicity, there are plenty of operational-level difficulties for conversation designers in the maintenance. When a chatbot reaches a certain size, it becomes extremely difficult to handle and follow any changes in the conversation model. Logic jumps (to create different paths under conditions) and loops (to go back to a previous element of conversation) are giving you a wide range of flexibility in this closed environment, but they often make the conversation difficult to handle.

Once any change has been implemented, you have to make sure that it did not cause any confusion in other convos and that each conversation path stayed “healthy”.

The following graphic shows the concept for an e-commerce chatbot and one of the possible paths a user might navigate (red arrows).

Conversation Flow

Botium Crawler simulates user clicks on all of the options in parallel, following all paths down until it reaches the end of the conversation or a certain criteria.

In the flow option you can see the visual representation of the conversation model of your chatbot.

Visual representation of the crawler

This is something you can most probably see on your conversation design platform as well.

In the Crawler script view you can see each convo path separately.

Crawler script view

As long as you have a green tick on the side, it means that the crawler successfully reached the end of the current path, without any failure. It suggests that the user will be also able to do the same in production.

Other cases with an exclamation mark are worth examining. In this case, the crawler could not reach the predefined depth in the conversation path. There could be more reasons behind this:

1. The conversation is stopped before reaching the maximum conversation steps

2. Wait for prompt

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2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

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4. An expert system: Conversational AI Vs Chatbots

1. Exit criteria

2. Entry point

Failing crawler session could also result in cases when the chatbot does not respond!

The additional benefit of the crawler is that all detected conversation flows along all paths can be saved as Botium test cases and utterance lists and can be used as a base for a regression test set.

In the end Botium Crawler helps conversation designers to untie the inscrutable threads of human communications and to examine each conversation path as an individual part of the final user experience.

Credit: Source link

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