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Quantitative Feedback for Training



The system needs to be trained by the user. The user does not want to invest a lot of effort in this process.

Example of a low-effort quantitive feedback option (thumbs up or thumbs down) included in a conversational flow with a chatbot
By prompting for a quick thumbs up or thumbs down, the system can get quick and easy feedback from the user to train itself with.


When providing an output, the system prompts the user to rate the quality of that output. This could be in the form of a simple thumbs up / down, or require a little more granular feedback in terms of a star rating or other score. 


Whether this is fed directly back into training the algorithm or is captured for analysis outside the system will depend on the specifics of the implementation. If the feedback is being captured for analysis, then it makes sense to prompt the user to also provide Qualitative Feedback for Training, with that feedback mechanism triggered by a negative quantitative feedback.