Flow settings

Learn to easily configure settings of your intent and its flow in your NLX workspace

What are flow settings?

Each flow created in your NLX workspace has crucial settings for workflow construction, customer intent recognition, and MCP-enablement before building and deploying an application or using agentic features that will access the flow.

A flow's Settings setup provides options for proper intent recognition and flow invocation, MCP-enablement, using slot variables, and assigning languages for translation.

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Add/duplicate flow

• Select Resources from workspace menu > Choose Flows > Click New flow

• 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.

To delete a flow, select the gear icon (Settings) within your flow's Canvas. Choose the Advanced tab > Select Delete flow option and confirm. If the intent flow is attached to an application(s), create a new build and re-deploy to experience the change.

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Routing

Routing in a flow's settings defines how a flow can be invoked. It provides the information LLMs and NLP engines need to recognize when a user’s intent matches the flow. Routing includes two key fields: AI description and Training phrases.

If the flow is being assigned to one of the application’s default behaviors, or if it will only be triggered by redirect nodes in other flows (rather than a user’s utterance), you can leave Routing blank.

AI description

The AI description field is required for LLMs to recognize when to call a flow. This is useful when assigning the flow as a tool for agentic applications, the agentic Generative Journey node, or for an MCP Client. You may also enter an AI description along with training phrases for improved performance with the NLX NLP.

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

• From the Routing tab, enter a concise description explaining the purpose of the flow Updates an existing hotel reservation

• Be sure to turn ON the MCP toggle in the MCP tab, if making your flow available as an LLM tool

Training phrases

Training phrases is required for NLP engines to recognize when to call a flow. They comprise samples of what users might say to indicate their intent. Your NLP engine learns from these examples to recognize real-world variations. For instance, in an OrderRoomService flow, phrases like “Order something to eat,” “I want room service,” or “Can I order food to my room?” train the NLP to match similar requests to this flow.

You may also enter an AI description along with training phrases for improved performance with the NLX NLP.

Instructions

The minimum number of training phrases recommended is 5, but providing a strong variety of user expressions results in better performance.

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

• Click AI settings tab > Enable Training phrases toggle

• Select + Add new training phrase

• When done adding phrases, click Save on the Canvas

Saved changes do not take effect with applications that are already deployed. To experience changes to training data with applications in production, create a new build and redeploy.

Optional:

• Skip translation (expand a phrase to view setting): Toggled OFF by default. Enable this in multi-language situations where the phrase would not make sense when translated into another language.

• Enable training: Toggled ON by default. If disabled, prevents users from being routed to the flow via intent recognition. Useful if the situation requires users only be navigated to the flow through deliberate redirects in other flows, or if the flow is set to a default behavior on the application. Essentially, this prevents the flow from being invoked by a user

• Generate using AI: Quickly generate your training dataset with AI

• Upload/Download: Allows for a JSON or CSV to be ingested or downloaded with training phrase data. Tap the CSV/JSON button beside the Download link to swap formats

Generate with AI

Want to create your training data in record time? Use the Generate using AI feature:

• Create a sample set of at least 1 training phrase to allow the AI to learn and expand on phrases with similar characteristics

• Click Save

• Select Generate using AI to create phrases in batches of five

• Modify before or after adding the phrases to your pool

• Repeat the process as needed (recommended pool size: 5-30)

If you have slots you'd like to reference in generated training phrases, include them in your sample set first.

Best practices

Predicting what users might say to in order for the right flow to be invoked is a challenging task. Users may say a lot or say very little when explaining what they want to accomplish.

Take a deep dive on organizing intent flows and developing training data with our favorite best practices.

Length

To start, keep training phrases short. AI models are able to identify filler words , misspellings, uncapitalized words, and politeness from user utterances, so no need to worry about creating multiple variations to account for them:

✔️place an order for room service

✔️would like to order room service

❌Can I please place an order for room service for my family this evening?

Leverage slots

Slots help you capture dynamic info that determine the parameters to a user's request or choice. Small/medium/large or yes/no are common examples. Introducing slots into training phrases can reduce creating different intents that would have similar training phrases or prevent the need for drafting multiple variations of a single training phrase:

✔️would like a {size} pizza

✔️look up {accountType} account balance

❌look up savings account balance; look up checking account balance, etc.

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Model Context Protocol (MCP)

MCP in a flow’s settings allows you to configure the flow as a tool that can be invoked by a Large Language Model (LLM). This can be used in several ways:

• When exposing your flow to an MCP client (with NLX serving as the MCP Server)

• When creating an agentic application in NLX

• When assigning the flow as a tool within the Agentic Generative Journey node

By enabling MCP, the LLM can recognize when to call the flow and execute the task it defines.

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

• Click MCP tab > Enable MCP toggle > Click Save

Input schema (optional): Define input schema, which specifies the variable(s) the LLM should collect before invoking the flow. This schema helps guide the LLM in gathering the right information.

For example, a flow designed to provide restaurant recommendations might use input schema that includes cuisine and location. The LLM would capture these details from the user and pass them into the flow to deliver more relevant results.

• 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

• 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:

MCP variable (cuisine) being passed to a custom API payload and referenced in a Basic node message

Be sure to also include an AI description for the flow and turn on the MCP interface setting on an application that will use MCP-enabled flow(s).

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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.

Slots must be attached to a flow to be used within its training phrases or within Canvas nodes.

• 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:

Built-in slot type	Use
NLX.Country	When a user response should be any country
NLX.Date	When a user response will use words to represent a date. Converts words to standard ISO-8601 format. Recognizes words such as "now" or "today" to mean the complete date of the current day. Input such as "this week" or "next week" will map to the first day of the week. ISO-8601 format begins its week on Monday and ends on Sunday. Input such as "next month" maps to the last day of the following month
NLX.Duration	When a user response should be words that represent duration (e.g., "two days," "30 seconds"). Converts words into numeric duration
NLX.Email	When a user response should involve words and characters to represent an email address. Supports additional characters such as underscores, hyphens, periods, and plus signs
NLX.FirstName	When a user response should use a word to represent a first name
NLX.LastName	When a user response should use a word to represent a last name
NLX.Number	When a user response should only use numeric words. Converts words to digits
NLX.Ordinal	When a user response should use ordinal words (e.g., "first" or "1st") to represent ordinal position
NLX.AlphaNumeric	When a user response may use a combination of letters and numbers
NLX.PhoneNumber	When a user response should use numeric words to represent a phone number. Converts them to a numeric string
NLX.Text	When a user response may consist of any words or characters
NLX.Time	When a user response should use words that represent time. Converts them to standard time format. You may use the Define node to parse information in flow, if needed
NLX.Url	When a user response should use words to represent a URL. Converts them to standard URL format
NLX.City	When a user response should be any global city
NLX.USCity	When a user response should be a United States city
NLX.USState	When a user response should be a United States state

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Languages

Need a list of available languages? See Supported 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.

Auto-translation

• Select Manage translations next to a supported language

• Choose Auto-translate all > Confirm

Manual translation

• Select Manage translations next to supported language

• Type translation for each training phrase

• Click Save

Optional:

• Expand training phrase > Mark as translated overrides auto-translation

• Expand training phrase > Skip translation if you do not want the phrase translated or no proper translation exists

To apply optional settings to all phrases, use the Mark all as translated or Skip all translations links above the list.

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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

• Click Save to restore a version

Restoring a version of a flow restores nodes patterns on the Canvas as well as the flow's settings from the previous save state.