← Back to portfolio

Viva Republica (Toss) · Mar 2026 – Jun 2026

Ad Review Agent

Role · Design and development

+25 ppaverage accuracy gainad-copy typo policy pilot · mean gain of the refinement and held-out sets (36 ads)

01Background & Goals

  • Ad policies are often a line or two, but an AI reviewer needs a guideline with criteria, boundaries and examples, and people had to write one for every policy.
  • When the guideline was stricter or looser than real reviewers, it rejected good ads or missed violations, and finding where it went wrong was hard.

02Key Challenges

  • C1 Turning a short policy into workable review criteria
  • C2 Closing the gap between the guideline and reviewers with data
  • C3 Avoiding overfitting to the refinement data

03Contributions

Automatic guideline generationC1
  1. Found the basis in the policy source text and turned a short policy into a guideline with decision criteria, violation/OK boundaries and policy references
Data-driven refinementC2C3
  1. Had AI compare the guideline's decisions with reviewers', find the patterns behind the disagreements and revise the guideline (e.g. marking compounds, amount units and ad-style phrasing common in ads as OK)
  2. Split refinement and held-out data to confirm the improvement holds on unseen ads
  3. People only checked the revisions; nobody edited the guideline by hand

04Tech Stack

Framework / Platform
LLM, Prompt engineering
Methodology
Data-driven prompt refinement, Holdout validation

05Results

  • Raised average accuracy by 25 pp on an ad-copy typo policy pilot (36 ads)
  • Accuracy also rose on held-out ads never used for refinement, so the gain holds on new ads
  • This approach became the prompt-refinement cycle of the Inspection Automation Platform