The goal is not a universal ranking. A useful review explains when a tool helps, for whom, and under what conditions.
A review should include
- Test date and version
- Real task and input scale
- Environment and important settings
- Output quality and validation method
- Failure cases and recovery cost
- Price, time, and learning cost
- Clear recommended and not-recommended boundaries
Material without direct testing should be labeled as observation or pending verification, not presented as personal experience.
Related: How to choose an AI tool and Projects.