Vidiskiu
commented
3 weeks ago Overall Point: 5.3
Functional Complexity: 0.7
Enhancing prompt for auto review reply and categorizing reviews is moderately complex as it requires understanding of the review content and user experience.
Technical Complexity: 1.2
Achieving a higher rate of auto review reply accuracy involves natural language processing and machine learning, which is technically demanding.
UI/UX Complexity: 0.4
The UI/UX aspect is probably not extensively affected, given the backend-centric nature of the issue. However, minor adjustments might be required to present categorizations or any new feedback to the user.
Data Manipulation: 0.8
Manipulation of review content data is required, but it builds on existing systems and thus is not maximally complex.
Testing: 0.3
Testing for accuracy of natural language processing requires extensive and carefully crafted test cases, but does not necessarily represent a maximum complexity.
Dependencies: 0.4
Dependencies may include NLP libraries or services, but as these are improvements to an existing system, they should not induce maximum complexity.
Risk and Uncertainty: 0.5
There is high risk and uncertainty involved due to the nature of machine learning accuracy and reliability in NLP, which can have unpredictable results.
User Impact: 1
Improving the accuracy of auto review replies has a high impact on user experience, efficiency, and satisfaction with the service, and is thus scored at the maximum within the defined range.
Problem: we still have lot of issues with the auto review reply suggestion Solution: Enhance the prompt and provide basic categorization for bad review Measurement:
Current evaluation:
Current analysis: