AI Evaluation Framework for Newsletter Content

• Developed systematic approach for evaluating AI-generated content

• Created "Golden Dataset" benchmarking methodology

• Implemented LLM Judge system for objective quality assessment

  While leading a team of three engineers on AI-powered newsletter recommendations, I identified and addressed a critical gap in our AI development process. After experiencing low adoption of a previous AI feature due to quality issues, I created a comprehensive evaluation framework that transformed how we assess AI-generated content.

  The framework combines curated high-performing subject lines with an LLM Judge system that scores AI suggestions against proven successful content. This approach delivered quantifiable metrics for previously subjective evaluations, enabled systematic pre-production testing, and provided ongoing performance monitoring. Most importantly, it built newsroom trust by making AI evaluation transparent and consistent.

  This methodology contributed to the future adoption of AI tools across the newsroom and created a reusable evaluation standard for multiple teams. The project demonstrates my ability to bridge technical implementation with editorial needs—creating systems that ensure AI enhances rather than replacing journalistic workflows.