Flow settings

What are flow settings?

Each flow created in your NLX workspace has crucial settings for workflow construction, customer intent recognition, and LLM training before building and deploying an application that will use the flow.

A flow's Settings setup provides options for adding advanced AI support, training data, using slot variables, and assigning languages for translation.

Add/duplicate flow

• Select Flows from workspace menu > Choose New flow option

• Provide a name (no spaces or special characters) > Click Create flow > Click Save

To duplicate an existing flow, choose the Settings icon within the flow's Canvas toolbar > Click the Advanced tab > Choose Duplicate flow. As flows cannot be renamed, you may duplicate an existing flow and provide the clone the desired name.

AI settings

AI settings is where you can provide necessary data for LLMs to recognize customer intent and invoke the correct flow, or to support integrating your flow as a service with an LLM through Model Context Protocol.

AI description

The AI description field is required for LLMs to understand the purpose and context of a flow so it understands when to invoke the flow.

• Select gear icon (Settings) within your flow's toolbar

• From the AI settings tab, enter a concise description explaining the purpose of the flow (e.g., Provide a restaurant recommendation) into the AI description field

Training phrases

Training phrases comprise samples of what users might say to your conversational AI application that in turn invokes a flow to satisfy their intent. AI models (NLP and LLM) become trained on these samples to better identify customer intent and invoke the appropriate flow.

For an OrderRoomService flow, training phrases such as 'Order something to eat,’ 'I want room service’ or ‘Can I order food to my room?’ helps AI recognize that similar expressions from a user should invoke that flow.

Model Context Protocol (MCP)

MCP (Model Context Protocol) allows you to expose your NLX workflow(s) to a supported Large Language Model (LLM), allowing the LLM to follow and execute a service or task defined in any flow. In this way, NLX acts as the MCP Server, while the LLM becomes the MCP Client.

Variables required for the flow to work that should be extracted by the LLM in conversation and passed along to NLX are set up first in the flow's Settings.

For example, if a flow is to provide restaurant recommendations to a user, the structured MCP schema could contain the variables cuisine and location that are captured by an LLM and passed along to NLX:

• Select gear icon (Settings) within your flow's toolbar

• Click AI settings tab > Enable MCP toggle

• Provide a unique and concise input name (no spaces or special characters)

• Enter the input schema containing necessary variable(s) that will be set and passed along by the LLM interfacing with a user

• Expand each property variable defined in your MCP input schema > Expand their settings > Enter a brief description in the property's Description field for the LLM to understand the purpose and context of each (e.g., location property might have the accompanying description for a restaurant recommendation flow: The location where you want to find a restaurant)

• Click Save

On any node of the flow, enter an open curly brace { and reference the MCP input variable you want to use as an output in messaging, payload fields, Split node conditions, etc:

Attached slots

Slots capture parameters from user responses and are deemed required information for completing a process outlined in a flow. The majority of user nodes in a flow are likely to be made up of User choice nodes or Generative Journey nodes that resolve these parameters while your conversational AI guides a user through a self-service task.

If a user wishes to book a stay at your resort, you might use slots to resolve the check-in date, check-out date, number of guests, name of guest, and so on.

• Select gear icon (Settings) within your flow's Canvas toolbar

• Click Attached slots tab > Select +Attach new slot

• Choose slot(s) from the dropdown (organized by Custom then Built-in types in your workspace)

• Provide a name for the slot to be referenced when in use

• Click Save

Optional:

• Examples (expand an attached slot to view setting): Add sample phrases that users may provide (e.g., for the built-in NLX.Time slot, you might add "7 pm," "7:00pm" or "7 in the evening" as examples for use with the Generative Journey node)

• Sensitive (expand an attached slot to view setting): Toggled OFF by default. There may be cases where a user's selection may reveal personally identifiable information (PII). For user privacy, you can enable the Sensitive setting so user input for this slot is not stored in NLX conversation logs

Custom vs built-in slots

Custom slots have values that are small in range, are customizable to your use case, and may also be visible choices for user selection in chat (text-based) applications. Custom slots are created within your workspace using the Slots resource.

Examples:

• Yes / No

• Small / Medium / Large

• I want to signup / I want something else

Built-in slots have standardized values that may be large or infinite in range or abstract:

Languages

Languages lists all languages assigned to your flow, allowing for easy management of translations to any messaging contained within the flow and/or training phrases.

• Select gear icon (Settings) within your flow's Canvas toolbar

• Click Languages tab > Select +Add new language

• Choose language(s) from dropdown

• Click Save

Managing translations

When new languages are added via Translations, they may be applied globally to resources in your NLX workspace. However, you may add/remove languages at the flow level to override your workspace's language settings.

Versions

You may easily restore or view past versions of a saved flow:

• Select gear icon (Settings) within your flow's Canvas toolbar

• Click Versions tab > Choose version to preview