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Making AI Chatbots:
Best Practices (Advanced)

Here are more design tips if you want to use the Juji Platform to optimize your AI chatbot performance and deliver superior user experience.

Use Proper Chatbot Request

Currently Juji supports two main types of chatbot request: choice-based and free-text. The former collects structured user input, while the latter elicits open-ended, free-text user input.

If your goal of a request is to guide users to different directions or gathers user input to a limited set of options, use the choice request. It is deterministic, quick, and almost error free.

If your goal is to elicit open-ended user input, e.g., their insights or unanticipated responses on a particular topic, use a free-text request. Because a free-text request is intended to gather open-ended user input, be aware of potential highly diverse user digressions. To handle such user digressions, make sure that you choose a right Juji built-in dialog to handle such situations automatically. You can of course write custom chatbot actions to handle specific user input that you wish to capture and address.

Use Proper Chatbot Trigger

Depending on your chatbot application, the tip below helps you decide whether you use a contains-keywords or is-similar-to trigger when you customize your chatbot actions.

In general, contains-keywords will help better match a user input syntactically as long as the input contains the keywords. In contrast, is-similar-to will help better match a user input semantically regardless of its syntactic form.

Assume that your chatbot asks What's your favorite fruit?. You define a trigger contains-keywords to capture particular kind of fruit like strawberry.

Keywords Trigger

As long as a user input contains a keyword that matches with the fruit name you put into, the trigger will fire. For example, it will match a user expression I really like strawberries:

Keywords
Trigger

It will also match a user input with very different meaning I like fruit except strawberries:

Keywords Trigger

This is because both expressions contain the keyword 'strawberry` regardless their use with different semantics. Note that Juji automatically handles stemming and lemmatization in keyword matching to support the matching of word variations.

However, a contains-keyword trigger would not match strawberry with blueberry because these are very different keywords:

Keywords Trigger

In contrast, if you define a trigger is-similar-to with examples such as I like strawberries:

is-similar-to trigger

This trigger will match a user input I like blueberries because this expression is considered semantically similar:

is-similar-to trigger

With a high enough matching threshold (e.g., 90%), the trigger will not match semantically different input such as I dislike strawberries even though it contains the word `strawberries'.

In short, a rule of thumb is that if you care about capturing the semantics of a user input more accurately, use is-similar-to where you can give words, phrases, and full sentences as examples. If you care about capturing a particular word/phrase regardless how it appear syntactically in an input, use contains-keywords.

In the near future, we will support the use of both in one trigger.

Give Sensible Labels

The label of a free-text request is a very important piece of information. It is used for multiple purposes. As described below, it is used to

  • summarize a chat topic (i.e., displayed on the topic card in the left panel)

  • search for built-in Juji dialog

  • translate user inquiries in context

  • index user answers in an audience report.

Search for Built-in Juji Dialog

A request label is always used to find a matched Juji built-in dialog to handle conversations around the request. For example, Juji has a built-in dialog that handles diverse user responses to the request What are your hobbies. To find such a built-in dialog, Juji uses the label entered with the request. Giving a sensible lable can better help Juji find the right built-in conversation, which can then handle diverse user responses on that topic with no or little customization required.

In particular, we suggest that you give short, concise labels to distill chatbot requests, which facilitates the finding of matched built-in dialogs. For example, if your chatbot is intended to ask a user about his/her opinion about an event, it might give a long description of the event before posing the question. In such a case, put the long and full description in the request, but keep the request label short and concise as shown in the example below:

A sensible label

Here is another example showing that the request wording is long while the label is kept short and to the point:

A sensible
label 2 - short

In case you don't think the label you entered retrieves a suitable built-in dialog, you can always browse Juji dialog library to search for a more suitable built-in dialog.

Handle Context-Sensitive User Inquiries

In a conversation, a user may pose a context-sensitive inquiry. For example, a chatbot asks Everyone loves movies. What's your favorite movie?. A user may ask a reciprocal question instead of answering the question What's yours. In such a case, the chatbot must first translate this user inquiry What's yours into a full inquiry What's your favorite movie based on the context before processing the user inquiry.

Currently Juji automatically translates a context-sensitive user inquiry based on the label instead of the request itself. This is because the label often captures the core question. A translation based on the core question can avoid unnecessary noises. Below shows two example requests and their labels, which are used for handling context-sensitive user inquiries.

Example I

In this example, the label captures the core of the request but not additional description.

A sensible label

Based on this label, below shows a chat fragment that handles a context-sensitive user inquiry on this topic:

A sensible label

Example II

Here is another example showing the label is just the stem of the question:

A sensible label

A conversation fragment shows that how the chatbot handles a context-sensitive inquiry on this topic:

A sensible label

Label User Responses in Audience Report

Another use of a request label is to label the user responses to the request in the audience report. Below is an example of audience report. It shows various user answers to the above two requests. As shown, both labels mentioned above are used to label the corresponding user answers.

A
sensible label - label user responses in audience report

Since the labels are normally a concise description of requests, using them to label user answers in the audience report makes the report more consumable.

See these instructions to download an audience report.

Reuse or Share Juji Chatbots

Creating a custom chatbot often takes time and effort. Juji allows you to easily reuse or share a Juji chatbot in one of the two methods below. Below we also indicate the key difference between these two methods.

Clone Juji Chatbots

In some cases, you may want your chatbot to behave slightly differently for different audiences. For example, one chatbot needs to redirect to a different URL after it is done chatting, while the other chatbot may generate a unique code at the end of the chat. To reuse most of the shared chatbot content, you can easily clone a chatbot as shown below and then deploy different chatbots for different audiences.

Clone a
chatbot

Export/Import Juji Chatbots

Once you have created a chatbot on Juji, you may want to share it with others, for example, a fellow chatbot designer who could help polish the chatbot or a client who wishes to buy your chatbot but manage the chatbot him/herself. On the other hand, you may want to create a chatbot based on another designer's chatbot instead of creating one from scratch.

On Juji, you can easily do so by exporting your chatbot so you can share it with others. The following screenshot shows that you can click on the ellipsis (...) menu button located on the top right corner of the card representing a chatbot you wish to export. Then click on the Export button. This action will automatically download a file named *-export.juji onto your computer, where * is the project name associated with the chatbot. For example, if we export the restaurant reservation chatbot in the screenshot shown below, the exported file name would be Restaurant-Reservation-export.juji

Export a
chatbot

If you wish to import a Juji chatbot, just click on the + AI Helper button. As shown below, instead of creating a chatbot from scratch, you can use the Import tab to import an exported *.juji file. After importing a chatbot, you can then customize, deploy, and manage the chatbot as if it were created by you from scratch.

+ AI Helper
button

Import a
chatbot

Tip: Difference between Clone and Export

Although both Clone and Export allow the sharing and reuse of a Juji chatbot, the key difference is that Clone allows the reuse within the same Juji account, while Export allows the reuse of a chatbot across different Juji accounts (e.g., yours and your teammate's).

Embed Juji Chatbots in Third-Party Apps

While Juji Studio provides an easy way to author, test, deploy, and manage an AI chatbot, Juji API provides the flexibility of integrating your chatbot into any third-party applications, such as a web or a mobile application.

The Juji platform supports the seamless, combined use of Juji Studio and Juji API. For example, content creators such as social media marketers, product managers, and sales professionals can use Juji Studio to customize the content of a chatbot, deploy and manage the chatbot. On the other side, the IT or engineering team can use the Juji API to "embed" the deployed chatbot into a brand's own business applications, controlling the look and feel of the chatbot and making the chatbot "native" to the application. This separation of duties gives each team the freedom to operate while allowing them to easily collaborate on the same chatbot.

Such integration is also straightforward: just use the Juji provided standard API to embed a Juji chatbot into a third-party application, without the need of downloading and installing any code or SDK. Once a chatbot is embedded, the business team who designs the content or workflow of a chatbot can use the Juji Studio to easily manage and update the chatbot without requiring the IT or engineering team's effort to update the embedded chatbot.

What's Next

Want to power up your chatbot with AI and get some magic going? Juji has you covered. Check out more chatbot best practices or venture into Juji IDE, which is an interactive development environment that enables you to customize a chatbot much more deeply than what you can do with Juji Studio.