AI Resolutions Log


Context LLM Records report lets you see all inbound and outbound messages happening in AI Chat. This is a key tool for enhancing content.

Unlock the power of this report to uncover end user inputs resulting in "no_match" responses. These instances indicate when our AI Chat didn't find a suitable answer to a user query. By analyzing trends in "no_match" responses, you gain valuable insights into popular questions that need attention. Armed with this knowledge, you can create and refine new responses to enhance user confidence and satisfaction, driving better engagement and outcomes.

This report replaces the following reports:

  • NLP Input Response Analytics report

  • NLP traffic Log

Report Components


Page Name

Name of the specific chat page

Page Type

Channel where the conversation was recorded.

Conversation Code

All messages from a given user on a given date (local time), on a given bot page.

End User Code

Unique users. A customer who holds conversations on multiple dates is still a unique end-user.

Message Code

Unique code of the message

Message Type

  • End User Button: Identifies if a user clicked on a button that continued the conversation flow

  • End User Quick Reply:

    • Identifies if a user clicked on a quick reply that continued the conversation flow

    • Identifies if a user provided a quick reply, for example, a number to continue the conversation flow

  • Bot Response: automatic response made by virtual assistant

  • End User Text: text input sent by the end user

Message Text

A specific message sent by either party


The purpose or goal behind the end user's message.

Intent Category

The broad classification or theme of the user's intent

Intent Sub Category

A more specific classification or subset within an intent category.


The answer provided by the AI Chat.

Response Name

The identifier or label assigned to a specific response.


  1. Search by ‘no_match’ Intent to identify topics without a content. Use the Dashboard to create new responses.

  2. Filter by Intent Category to get a better understanding of a specific Category.

    1. Ticketing

    2. Communications

    3. FB

    4. Parking

    5. Activities

    6. Health

    7. On-site

  3. Monitor unique user interactions over time to identify patterns in engagement and tailor responses accordingly. Use unique identifiers to personalize interactions and track individual user journeys.

  4. Filter by a conversation code to see a given conversation on a given date and time

  5. Determine which channels and pages are most effective for engaging users and driving conversions.

  6. Categorize intents to better organize and analyze conversation data. Identify common themes and topics to optimize response strategies.

  7. Track response performance metrics such as Response Name, Response Language, Content Type, and Content Group. Analyze which types of responses are most effective in driving desired outcomes and adjust content strategies accordingly.

  8. Review specific message content to understand user inquiries, concerns, or feedback. Use sentiment analysis and keyword tracking to identify trends and address common user issues proactively.

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