Data Collection
First comes the data collection phase, where this AI content detector gathers a diverse and representative dataset. The dataset involves examples of both human and AI content because that’s how this web utility will be able to differentiate between both types of text.
Feature Extraction
The next stage is feature extraction, where our AI detector extracts relevant characteristics or patterns from the gathered data. In this case, those characteristics or patterns will involve things like the frequency of words, sentence structures, etc. The AI writing detector uses these patterns to predict the classification of AI-generated and human-written text.
Model Training
Here, the AI detector uses the extracted patterns from the previous stage to train its AI detection model. This process happens in different iterations, where the model makes predictions and gets feedback. Based on the provided feedback, our AI text checker adjusts its internal parameters.
Actionable Insights
The last stage is the testing phase, where this AI detector operates its functionality on unseen data to check its capability. The AI writing checker uses its training to provide actionable insights into the extracted features. In this case, those actionable insights will involve the identification of AI content.