• Entities that exceed the set maxLength value are not accepted. For example, if the entity value is “123456” and the maxLength is set to 7 and the customer says “78,” then since the newly extracted entity is “12345678” and the length is now 8, the bot treats the new entity as a noMatch and keeps the remainder as “123456.”
• Correction of cases that are not explicit or are in the middle portion of the extracted entity. In the following examples, the previously extracted entity was 1299554464.
  
  Examples of working corrections:
  • “No change the last two digits from 64 to 62”
  • “The last two digits should be 62”

Example of non-working corrections:

• • “No 62.” The LLM cannot determine what to alter.
  • “No, I meant 62.” The LLM cannot determine what to alter.
  • “Change 55 to 44.” This entry is difficult for the LLM to determine because it is in the middle of the entity.
  • “The entity should start with a 5.” The LLM may add a 5 to the start or it may correct “1299554464” to “5299554464” as expected.

• The LLM does not have issues with larger numeric values; however, doing so increases the chance of more difficult correction. Bigger numeric values are more difficult to correct in the start or middle of the value. Because of this limitation, Genesys recommends the multi-slot nature use, so if a credit card number is extracted, ask for the data in 4-digit chunks. Any mistake occurs within the last four digits, which makes it easier to correct.

Cases that work well are:

• Simple digit extraction of all lengths. For example, “My credit card number is 0123456789012232.”
• Digit extraction over multiple turns with digits in lexical format. For example:
  • Participant: “My card starts with 0011”  bot: “I got 0011 so far, please continue.”
  • Participant: “Then 7831”  bot: “I got 0011 7831 so far, please continue.”
  • Participant: “Seven one double oh” bot: “I got 0011 7831 7100 so far, please continue.”
  • Participant: “Finally, 3333” bot: “I got 0011 7831 7100 3333 is that correct?”
• Explicit corrections; for example, “Change the last two digits from 84 to 82.”
• The LLM treats “Double” as two of whatever comes after; for example, double 2 = 22. The ASR should convert this response to 22 in the first place. “Triple”/”Treble” is three of that digit and “quadruple” is four.
• The LLM treats “Oh” as “0” in expected situations, not in unexpected ones such as, “Oh sorry I meant.”

• Entities that exceed the set maxLength value are not accepted. For example, if the entity value is “A12345,” the maxLength is set to 7, and the customer says “67,” then because the newly extracted entity is “A1234567” and the length is now 8, the bot treats the new entity as noMatch and keeps the entity “A12345.”
• Duplicated characters over multiple turns. If, on turn 1, the extracted entity is “AB78G” and on the next turn the customer starts with another “g,” the LLM can mistakenly return “AB78G” instead of “AB78GG.”
• Ambiguous corrections. For example, “No I said AZ.” An ambiguous correction can happen if, on turn 1 the customer said “A for apple, C 72” which was extracted as “AC72;” on the next turn they could make a difficult correction such as “No I said AZ.”
• The LLM does not have issues with larger alphanumeric values; however, doing so increases the chance of more difficult correction. Bigger numeric values are more difficult to correct in the start or middle of the value. Because of this limitation, Genesys recommends the multi-slot nature use, so if a passport number is extracted, ask for the data in 3-character chunks. Any mistake occurs within the last 3 digits, which makes it easier to correct. 

Cases that work well are:

• Alphanumeric extraction of all lengths with phonetic spelling of letters, simply stated letters, and numerals. For example, “My passport number is a for apple, b for beta, c for charlie, d 8909.”
• Alphanumeric extraction over multiple turns with digits in lexical format, for example:
  • Participant: “My membership number starts with AB11”  bot: “I got AB11 so far, please continue.”
  • Participant: “Then c for charlie and z for zeta”  bot: “I got AB11 CZ so far, please continue.”
  • Participant: “beta alpha”  bot: “I got AB11 CZ BA so far, please continue.”
  • Participant: “Finally, 99”  bot: “I got AB11 CZ BA 99 is that correct?”
• Explicit corrections. For example, “No the last letter should have been Z for zeta not c.”
• The LLM treats “Double” as two of whatever comes after; for example, double 2 = 22. The ASR should convert this response to 22 in the first place. “Triple”/”Treble” is three of that digit and “quadruple” is four.
• The LLM treats “Oh” as “0” in expected situations, not in unexpected ones such as, “Oh sorry I meant.”

• Addresses
  • Address format per specific country standards. The bot participant must rely on the description that you provide to ensure the proper format.
  • Casing: The correct casing is typically correct; however, the extracted entity may occasionally return as all lowercase or all uppercase.
• Emails
  • Incorrect identification of custom domain names across multi-turn conversations. More common, accurate returns occur when the emails are provided in a single turn.
  • Cases where ASR transcriptions fail to convert dashes, dots, and underscore.
• Names
  • Missing or rearranged characters when long names are spelled out.

The model output after each call contains two parts: the extracted entity and a Boolean, whether the extraction is complete or not, on which the entity detection status is in progress or completed. For freeform, the bot uses the provided description.

• The model uses the description to judge whether the entity is captured or if parts mentioned in the description are missing. A description should ideally contain what the entity is about and what other subentities or parts of an entity that it must contain.
• It is also possible to skip ahead. If the customer explicitly says something similar to:  “I’m done, that’s it, that is all, I don’t have it, I don’t know,” and so on, the extraction status changes to completed and overrides the subentity collection based on the description.

In these examples, the slot is person_name to describe the first name and last name of a person. 

• The bot participant specifically mentions what part of the entity is provided.

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• The bot participant only mentions which part of the name is provided in the initial turn; the model assumes that the next sub-entity that the bot participant provides is the last name.

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• Both subentities are provided at once and the status changes to completed after the first turn.

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• Status stays in progress because the bot participant specifies that the entity being provided is a middle name, not mentioned in the description; the next entity that the bot participant provides is assumed to be the last name, as expected.

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• Although the bot participant doesn’t specify, and “van der” should be assumed as a last name, it probably is not, since “van der” is a commonly used surname prefix and not an actual surname.

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In these examples, the slot is delivery_address to describe a delivery address including house number and PIN code. 

• Conversation stays in progress until both the house number and PIN code are provided; adds house number at the start of the address and PIN code at the end.

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• Although a PIN code wasn’t provided, the status changes to completed because the bot participant indicates that they are done. This status does not occur when the participant doesn’t say “that’s it“ and the status only goes to completed when a PIN code is provided.

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These examples describe early exit behavior scenarios for a delivery_address slot that describes a delivery address with a house number and PIN code.

• Example 1 Early exit example

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• Example 2 Early exit example.

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• Example 3 Early exit example.

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• Example 4 Early exit example.

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For information about sample conversations that demonstrate how free form slot capture works, see Free form slot capture examples.