Question¶
Description¶
A Question Node is used to ask a question that requests specific information from the user.
After a question is asked and the user answers, the answer is validated according to its type. If it passes, the answer is valid and stored, and the conversation continues.
Attached Flows
When a user input is received, it will be scored by the NLU. If an attached Flow has an Intent, which scores higher than Intents in the current Flow, the attached Flow is executed. This occurs before validation of the Question Node is completed. See Attached Flow.
Watch this Episode of Cognigy Sessions for a technical deep dive
Question Types¶
Question Nodes have a selection of types that determine the validation used before a conversation continues.
| Type | Expected user input to answer question | Example |
|---|---|---|
| Text | Any text input | |
| Yes / No | A positive or negative response | |
| Intent | One of the trained Intents must be identified from the user's response | |
| Slot | A System Slot or Lexicon Slot must be detected within the user's response. The slot is defined by name. | |
| Date | Any date (system-defined) | |
| Number | Any number (system-defined) | |
| Temperature | Any temperature (system-defined) | |
| Age | Any age (system-defined) | |
| Duration | Any time duration (system-defined) | |
| Any email address (system-defined) | ||
| Money | Any monetary value (system-defined) | |
| URL | Any reference/address to a resource on the Internet for example, http://example.com | |
| Percentage | Any percentage (system-defined) | |
| Regex | Any custom data format defined by a regex expression must be detected in the user's response | |
| Data | Any data (input.data) input | |
| xApp | Any xApp input | |
| Custom | Any input | |
| Pattern: License Plate (DE) | A pattern for the German vehicle registration plate. This license plate is a unique alphanumeric identification tag displayed on a vehicle. It consists of letters, numbers, and sometimes special characters, for example, ö, ä, or ü. License plates serve as a means of identifying and registering vehicles, providing important information such as vehicle ownership, registration details, and compliance with legal requirements. |
M-345, x1Y2Z3, D 12345C |
| Pattern: IBAN | A pattern for the International Bank Account Number (IBAN) | DE12345678901234567890 |
| Pattern: Bank Identifier Code (BIC) | A pattern for the Bank Identifier Code (BIC) | DEUTDEFF500 |
| Pattern: Social Security Number (US) | A pattern for the US Social Security Number | 123-45-6789 |
| Pattern: IP Address (IPv4) | A pattern for the IPv4 address | 192.168.1.1 |
| Pattern: Phone Number | A pattern for the phone number | +49 0000000000, 49 0000000000, +490000000000, (555) 000-000 |
| Pattern: Credit Card | A pattern for the bank card | 4111111111111111 |
| LLM-extracted Entity | Utilizes a chosen LLM to extract entities, such as product codes, booking codes, and customer IDs, from a given string. Go to the LLM Entity Extraction Options. |
Regex
Ensure that your regular expression starts with a / and ends with /g.
Example: /^1\d{7}$/g
Supported Data formats for System Defined slots
All data formats supported by the Cognigy NLU for system slot mapping are listed on the Slot Mapping page.
Channels and Output types¶
Question node output types carry the same functionality as the Say Node.
Date Questions
In case the Question Type Date has been selected, the Question Node will automatically render a Datepicker if the channel permits. Refer to Datepicker for more information.
LLM Entity Extraction Options¶
This section appears if you selected the LLM-extracted Entity question type.
Before using this Question type, set the Generative AI provider in the Settings. You can configure the Node to either use the default model defined in the Settings or choose a specific configured LLM.
| Parameter | Type | Description |
|---|---|---|
| Large Language Model | Select | Select a model or use the default one. |
| Entity Name | CognigyScript | The name of the entity to extract. For example, customerID. |
| Entity Description | CognigyScript | A sentence which describes the entity. For example, An alphanumeric string of 6 characters, e.g. ABC123 or 32G5FD. |
| Example Input | Text | Examples of text inputs. For example, My ID is AB54EE, is that ok?, That would be ah bee see double 4 three, I guess it's 49 A B 8 K. |
| Extracted Entity | CognigyScript | Examples of extracted entities. For example, AB54EE, ABC443, 49AB8K. |
| Additional Validation | CognigyScript | User input must meet this extra validation criteria, in addition to the built-in field validation, for example, Email, to be considered valid. |
Alternatively, you can add input examples in the Use JSON Editor code field. For example:
{
"My ID is AB54EE, is that ok?": "AB54EE",
"That would be ah bee see double 4 three": "ABC443",
"I guess it's 49 A B 8 K": "49AB8K"
}
Advanced¶
| Parameter | Type | Description |
|---|---|---|
| Temperature | Indicator | The appropriate sampling temperature for the model. Higher values mean the model will take more risks. |
| Timeout | Number | The maximum amount of milliseconds to wait for a response from the Generative AI Provider. |
Reprompt Options¶
Reprompt messages are automatically triggered if the question is not answered correctly,
such as when the expected type of input is not provided or a validation does not return true.
Reprompt Methods¶
You can select one of the following reprompt methods:
Simple Text¶
Outputs a simple text message to the user.
| Parameter | Type | Description |
|---|---|---|
| Reprompt Message | CognigyScript | The message to output if the given answer is invalid. For example, Not sure I understood this correctly. |
Channel Message¶
Outputs a comprehensive, channel-specific message to the user. This message includes rich-media, for example, images or audio. You can define distinct messages for each channel, such as Webchat, Voice Gateway, WhatsApp, and others.
| Parameter | Type | Description |
|---|---|---|
| Reprompt Message | Channel | The channel for which you want to configure the repromt message. You can select multiple channels. |
| Output Type | Select | The format of the content that delivers through the selected channel. Depending on the selected channel, a list of possible output types suitable for that channel will be presented. All channels support the text type, but support for other types may vary. |
LLM Prompt¶
Runs an LLM prompt and outputs the result in the format of a text message to the user. Before using this Node, configure a model in the LLM settings.
| Parameter | Type | Description |
|---|---|---|
| Large Language Model | Select | The channel for which you want to configure the repromt message. You can select multiple channels. |
| LLM Instructions | CognigyScript | The input or command, such as a prompt or system message, that is given to the Language Model to generate an output. |
| Transcript Turns | Slider | The number of conversation turns to include in the LLM chat completion request. By default, the value is 3. |
Execute Flow and return¶
Executes a specific Flow and returns to the question afterward.
| Parameter | Type | Description |
|---|---|---|
| Select Flow | Select | The Flow that will be executed as the reprompt message. |
| Select Node | Select | The Node from which the Flow should be executed as the reprompt message. |
| Parse Intents | Toggle | This setting determines whether the system should parse Intents separately in the executed Flow. By default, this setting is enabled. |
| Parse Slots | Toggle | This setting determines whether the system should parse Slots separately in the executed Flow. By default, this setting is enabled. |
| Absorb Context | Toggle | This setting determines whether the system should absorb the executed Flow's Context into the current Context. By default, this setting is disabled. |
Repeat Question¶
Repeats a question if a user gives an invalid answer. By default, this setting is enabled.
Reprompt Condition¶
Optionally, you can add a CognigyScript condition to determine whether a reprompt message is shown.
Skip on Intent¶
Skips the reprompt if an Intent was found in the input.
Result Storage¶
Question results are always stored in input.result.
If Store Result in Context is enabled, the Question Result will also be stored in the Context object.
If Store Result to Contact Profile is enabled, the Question Result will also be stored in the Profile object.
Escalation - Intents¶
Allows the conversation to break out of the Question Node if a specified Intent was found.
| Action | Description |
|---|---|
| Output Message | Outputs a message (equal to a Say Node) |
| Skip Question | Skips the Question and enters a specific value into the input.result object. |
| Go To Node | Goes to a specific Flow Node and continues from there (equal to Go To Node) |
| Execute Flow and Return | Goes to a specific Flow Node and returns to the question after (equal to Execute Flow Node) |
| Handover to Human Agent | The conversation is handed to an agent, who can help you finish the question step and hand it back |
Add intents that can trigger the "escalate on intent" function by typing the intent name into the "Valid Intents" field and pressing ENTER on your keyboard. Adjust the dedicated intent score threshold slider to the preferred setting so that the escalation will only occur if one of the listed intents reaches that score.
Escalation on Wrong Answers¶
Allows the conversation to break out of the Question Node after a number of incorrect answers were provided.
| Action | Description |
|---|---|
| Output Message | Outputs a message (equal to a Say Node) |
| Skip Question | Skips the Question and enters a specific value into the input.result object. |
| Go To Node | Goes to a specific Flow Node and continues from there (equal to Go To Node) |
| Execute Flow and Return | Goes to a specific Flow Node and returns to the question after (equal to Execute Flow Node) |
| Handover to Human Agent | The conversation is handed to an agent, who can help you finish the question step and hand it back |
It is possible to prevent reprompts when the escalation is happening.
The option "only escalate once" determines if the escalation only happens once on the threshold or on every input form the threshold on.
Handover to Human Agent¶
As of Release v4.4.0, we added the option Handover to Human Agent. Open the Node Editor and you will find this option as an escalation action for Intents and Wrong_Answers that offers the ability to escalate questions by creating handovers to a real human agent.
When this escalation is hit, the conversation is handed to an agent, who can then help you finish the question step and hand it back.
Reconfirmation Settings¶
Allows for answers to be reconfirmed before continuing. This is especially useful when using voice bots and reconfirming what the bot understood (for example, in Number questions when the user said "my number is three double five triple nine five six eight"). The answer given to the reconfirmation question has to be a yes/no style answer and follows the same rules as a Yes/No Question.
Reconfirmation Questions can contain a specific token ANSWER, which will be replaced with a short form version of the given answer (for example, "3 EUR" in a Money question). The short form answer is taken from input.activeQuestion.tentativeShortFormAnswer;
Reconfirmation Questions can have a specific re-prompt set, which will be output before the question if the answer to the question is not of yes/no style.
Advanced¶
The Question Node has two advanced settings that can be used.
Store detailed Results¶
This setting, when enabled, stores a more detailed JSON object under the result property of the input. This is useful in case more information is needed.
Skip if Answer in Input¶
When enabled, this setting will skip the Question if the answer is already provided in the input text.
Additional Validation¶
A CognigyScript condition which must return true in order for the answer to be considered valid. An example would be an additional validation on an Email Question of input.slots.EMAIL[0].endsWith("cognigy.com") which would guarantee that only cognigy.com email addresses pass the validation.
Result Location¶
The location of an answer is determined by default by the question type (for example, input.slots.EMAIL[0] for Email Questions). This can be overwritten using this setting (for example, input.slots.EMAIL would store all found email slots). If the result location doesn't return a value (= is falsy), the answer will be considered invalid.
Forget Question Threshold¶
This setting determines how long a user can have been "away" from the Node after the question was initially asked. With the default setting 1 this means that the question has to be answered on the next user input. If a user input comes back to the question at a later stage, it will be treated as if the question was hit for the first time and the question will be asked.
Question Nodes and Intent Execution
Question Nodes, by default, will be triggered repeatedly until a valid answer is provided. To avoid this, you can opt for an Optional Question or change the Intent Execution setting.
Slot Fillers
Questions can be combined with Slot Fillers to create a so-called "Missing Pattern". This mechanism keeps asking the user for the missing information in a very natural way, until all questions have been answered.
Question Information in Input¶
When a question is active, indicating that an answer is awaited, information regarding the question is added to the Input object.
"activeQuestion": {
"nodeId": "18b158bf-71a3-4d4f-a31f-812b1810f8af",
"type": "yesNo",
"lastExecutedAt": 2,
"forgetQuestionThreshold": 1,
"repromptCount": 1,
"escalationCount": 0
}
This information can be used to trigger specific actions on escalation or to jump back to the Question Node after an escalation.
AI-enhanced output¶
To use AI-enhanced bot output rephrasing, read the Generative AI article.
Answer Preprocessing¶
You can use various functions of the Text Cleaner class to preprocess the answer to a question before it is evaluated. This can be helpful, for example, when requesting a name using a text type question or when asking for a part number using a slot question.
In addition to the Text Cleaner functions, users have the option to rerun NLU after the cleaning process. This approach allows for tasks such as re-detecting slots or properly filling any remaining slots.