Fallback is a response or behavior that comes into play when a system, model, or bot does not understand a specific question or request. In summary, if the system cannot provide a relevant or even irrelevant answer to the user's question, it is considered a Fallback situation.

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For example, if a user writes "About the staff" and is seeking information on this topic, but the chatbot does not understand the question, this is a Fallback (Failure to Understand) situation.

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Possible issues arising from this situation include:

  • It leads to a poor experience due to failure to meet the user's needs.
  • The user may think that the bot is not working correctly and is not fulfilling its function.

For mismatch situations, the fallback intent is activated in our panel, defining the response. This way, user expressions that fall below the confidence levels of the bot are matched with the fallback. If the fallback response is not activated or defined, JetBot will not respond.

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For the user expression "About the staff" mentioned above, the bot's General AI Settings are 0.75-0.70 and Probabilistic AI are 0.65-0.65. Based on this information, a user expression with a match rate below 0.65 will align with the fallback intent.
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In the visual below, we can see that the user expression "About the staff" could match with an intent with a closest score of 0.26. Since the 0.26 score is below the defined confidence levels, the bot provides a Fallback response.

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If you want to see which intent matches with which expression, you can click on the button with three lines on the JetBot screen and then enter the user expression you want to test.

You can check Fallback situations in conversations through two separate screens in the Jetlink panel.

1-JetBot Training: After logging into the panel, you can go to the JetBot Training screen from the JetBot screen and filter by selecting "No Match" to view the match status.
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By clicking the "Train" button on the right side of the conversations below, you can open the non-matching conversation and select the intent you want to match through the "Assign Intent" option to perform training.

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2-Intent-Message Records Report: After logging into the panel, you can access the ''Intent-Message Records Report'' screen from the Reports Menu.

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From the opened report screen, you can select the date ranges for the report you want and download the report to your computer by clicking the "Generate Report" button.

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In the downloaded report, you can view the intents that match user expressions.

You can use the Conversation Number in the report to search for the relevant conversation on the Conversations screen. By clicking the "Train User Expression" link under the Fallback expression in the conversation content, you can open the JetBot Training screen and perform training on the expression as described in the previous step.

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If the LLM option is turned off in your bot, meaning it only uses NLP, the "I did not understand" status message described above applies. If the LLM is enabled in the bot, the Fallback response is provided through the LLM. The model first searches for matches using the classic approach. If it does not find a match on the custom-designed LLM Knowledge Base, the LLM Fallback message is triggered.

You can access LLM Fallback messages through the "Responses Generated by LLM" report.

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You can use several strategies to prevent fallback situations in chatbots:

  1. Comprehensive Training Data: It is important to train your chatbot with a broad and diverse dataset. Keep your data up-to-date and comprehensive to ensure the chatbot can respond to questions expressed in various ways by users.

  2. User Guidance: Provide guidance and suggestions to help users adhere to certain patterns while interacting with the chatbot. For example, you can use open-ended questions like “How can I assist you?” to guide the conversation.

  3. Integration of Question-Answer (QA) Systems: Integrating a QA system with frequently asked questions and answers can help the chatbot provide quick and accurate responses to such queries.

  4. User Feedback: Collecting and analyzing user feedback can help identify shortcomings and common issues users face with your chatbot. Use this feedback to continuously improve your chatbot.

  5. Ongoing Updates and Improvements: Regularly reviewing and improving your chatbot’s performance helps it adapt to new user needs and language changes.