Writing Effective Review Criteria

The quality of your review criteria directly determines the quality of your review feedback. Well-written criteria produce scores that align with expert human judgment and feedback that writers can act on immediately. Poorly written criteria produce vague, inconsistent results that erode trust in the review process.

The most important element is your guidance text. This is the instruction that tells the AI what to evaluate and what constitutes a high or low score. Compare these two examples: "Check readability" produces generic feedback about reading level. "Evaluate sentence length (target average under 20 words), active voice usage (at least 80% of sentences), paragraph length (3-5 sentences maximum), use of jargon (flag any industry terms not defined on first use), and Flesch-Kincaid grade level (target 8th grade or below)" produces specific, measurable feedback that writers can act on immediately.

Each criterion should evaluate one dimension of quality. Avoid combining multiple concerns into a single criterion -- "readability and SEO" should be two separate criteria. When a criterion evaluates multiple things, the score becomes an ambiguous average that does not clearly indicate what needs improvement. A low score on a combined criterion leaves the writer guessing which aspect is the problem.

Weights should reflect your actual priorities, not a balanced distribution. If brand voice matters four times more than formatting in your content, make the weights reflect that reality. Many teams default to even weights, which dilutes the signal from their most important quality dimensions. Uneven weights are not just acceptable -- they are usually more accurate.

Include examples of what high and low scores look like in your guidance text. "A score of 9-10 means: all sentences under 25 words, consistent active voice, no undefined jargon. A score of 3-4 means: multiple sentences over 40 words, frequent passive constructions, jargon used without explanation." These anchors help the AI calibrate its scoring and produce more consistent results across reviews.

Test your criteria with content you have already evaluated manually. Run a few pieces through the reviewer and compare the AI scores with your human judgment. Where they diverge, adjust the guidance text -- usually, making it more specific resolves the discrepancy. This calibration process typically takes three to five iterations before the reviewer produces scores you trust.

Revisit your criteria quarterly. Content standards evolve, and criteria that were well-calibrated six months ago may no longer reflect your current priorities. Schedule periodic reviews of your reviewer configurations to ensure they stay current and continue producing relevant feedback.

Finally, involve your team in criteria development. Writers who help define what "good" looks like produce better content because they understand the standards explicitly. Collaborative criteria development also increases buy-in -- writers are more receptive to feedback generated by criteria they helped create.