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TABLE OF CONTENTS
What are Freddy Answers’ learning sources?
Training a bot and making it sound factual and adapted to your brand’s tone is no small feat. To make this process simpler for you, Freddy Answers will use ML to suggest Commonly Asked Questions across different Topics and help you and your team identify what questions need new answers or better answers. It will further categorize the questions into three buckets: Unanswered, Improve Coverage, and Underperforming.
Before we dig deeper into what are Freddy Answers' learning sources, let’s learn more about what these categories mean.
Unanswered: Any question that has not been answered will appear here. It’s an indication that no content around that question exists in the “Answers” gallery page.
Improve Coverage: There are different ways of asking the same question. If a question has not been mapped to 5 different variants (i.e 5 other ways in which that question can be asked), Freddy Answers will categorize them under “Improve Coverage.” Goal? Give precise answers, no matter how a question is asked.
Underperforming: Any Freshchat FAQs or Freshdesk articles that have been downvoted 5 times or more, will be categorized as “Underperforming.” This is an indication that the answer was not helpful and needs to be re-looked at.
Sources Freddy Answers identifies commonly asked questions from
Freshchat FAQs: Any Freshchat FAQ that have been downvoted 5 times or more will be categorized under the “Underperforming” bucket.
Freshdesk Articles: Any Freshdesk knowledge base articles that have been downvoted 5 times or more will be categorized under the “Underperforming” bucket.
The answers you create for these suggested questions or any question typed by you, will reflect under the “Answers” page. Freddy Answers will scan through the “Answers” page to send precise answers to your customer’s questions. All your existing Freshchat FAQs and Freshdesk FAQs will also be auto-synced on this page.
