Rank Math Content AI sits inside Rank Math and generates outlines, paragraphs, meta descriptions, and SEO suggestions per post. Every generation lands in the Content AI history with a prompt, an output, and a usage credit cost. The history grows fast, especially on multi author sites, and the editorial review of which outputs actually shipped lives in Slack screenshots rather than on a queue.

SleekView Feedback reads the Content AI history rows and renders one card per generation. Each card carries the prompt, the generated output, the cost in credits, a vote count, a status pill (drafted, edited, shipped, killed), and a category pill for the generation type (outline, paragraph, meta, schema, FAQ). Editors land on one board, upvote the outputs that earned a slot in a real post, flag the hallucinations, and request prompt templates that would cover gaps.

Votes write back to a column you map on the Content AI history row. Future generations can be sorted by editorial score before relevance, retired prompts drop out of suggestions, and the team has a shared signal of which AI outputs deserve to ship rather than a Slack thread per author.

Rank Math Content AI puts a generation surface inside the post editor for every author. The leverage is real. The risk is that without shared review, the editorial team has no signal which prompts work in their voice, which outputs land cleanly, and which generations are quietly inflating the credit bill.

After a quarter you have thousands of history rows, a long tail of forgotten prompts, and no shared sense of what shipped versus what got rewritten. SleekView Feedback puts the Content AI history on a public board. Each card carries the prompt, the output, the credit cost, and a category pill for the generation type.

Editors vote on which outputs earned a slot in published posts, flag hallucinations, and request prompt templates that would cover gaps. Votes write back to a column on the history row, so future suggestion ranking respects editorial signal and the company prompt library stays curated. Credit cost stops being a black box.

Most importantly, the board becomes a training surface where new writers see which prompts produce shippable output in the team's voice, and the AI generation bar lifts over time without a separate onboarding program for every new author.