In this post, we demonstrate how PowerSchool built and deployed a custom content filtering solution using Amazon SageMaker AI that achieved better accuracy while maintaining low false positive rates. We walk through our technical approach to fine tuning Llama 3.1 8B, our deployment architecture, and the performance results from internal validations. Read More
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October 7, 2025The “educational context” challenge is real but often underestimated. Testing similar classification tasks locally shows that distinguishing between academic discussions of sensitive topics and actual threats requires understanding intent and context, which 8B models tend to struggle with consistently. The Holocaust education case is straightforward, but edge cases like student poetry with violent imagery or creative writing assignments on war scenarios create ambiguous classifications where even humans disagree.
The average 1.5-second response time for content filtering introduces noticeable delay in interactive AI experiences. When students ask PowerBuddy a question, waiting an extra 1-2 seconds for safety checks before responding diminishes the “seamless user experience” promise. The article doesn’t specify whether filtering operates in parallel with generation or if it stops the entire interaction.