Michael Truong, a technical writer, developed an AI-assisted editorial pipeline called editor-critique to automate the review process for his blog posts. His original workflow followed a score-first sequence where the model evaluated drafts against a rubric before generating feedback. While this approach effectively checked for structural completeness—resulting in a 23/25 score for one article—the feedback remained shallow, failing to identify significant issues regarding reader journey and context reliance.

The author discovered that assigning numerical scores too early caused the model to justify its assessment rather than critically engage with the content. By revising the pipeline to prioritize an editorial read-through before scoring, the system could identify deeper flaws, such as spoilers in titles, excessive reliance on private repository context, and gaps in evidence. The updated workflow now loads the draft, performs a cold editorial read, then assesses rubric dimensions to generate the final critique.

This sequencing shift transformed the reviewer's output. Instead of merely confirming compliance with stated criteria, the revised system provided actionable advice on what might break for a reader. The author distinguishes this as the difference between QA review, which validates artifact completeness, and editorial review, which evaluates audience impact. The author suggests that for future AI review tools, developers should prioritize an ungated initial analysis and ensure rubric scores act as a summary of findings rather than a substitute for reading. The author hypothesizes that this pattern of analysis-before-scoring is portable across other technical processes, including code and architecture reviews, to prevent models from overfitting to rubrics.

Michael Truong, a technical writer, developed an AI-assisted editorial pipeline called editor-critique to automate the review process for his blog posts. His original workflow followed a score-first sequence where the model evaluated drafts against a rubric before generating feedback. While this approach effectively checked for structural completeness—resulting in a 23/25 score for one article—the feedback remained shallow, failing to identify significant issues regarding reader journey and context reliance.

The author discovered that assigning numerical scores too early caused the model to justify its assessment rather than critically engage with the content. By revising the pipeline to prioritize an editorial read-through before scoring, the system could identify deeper flaws, such as spoilers in titles, excessive reliance on private repository context, and gaps in evidence. The updated workflow now loads the draft, performs a cold editorial read, then assesses rubric dimensions to generate the final critique.

This sequencing shift transformed the reviewer's output. Instead of merely confirming compliance with stated criteria, the revised system provided actionable advice on what might break for a reader. The author distinguishes this as the difference between QA review, which validates artifact completeness, and editorial review, which evaluates audience impact. The author suggests that for future AI review tools, developers should prioritize an ungated initial analysis and ensure rubric scores act as a summary of findings rather than a substitute for reading. The author hypothesizes that this pattern of analysis-before-scoring is portable across other technical processes, including code and architecture reviews, to prevent models from overfitting to rubrics.