Generative Engine Optimization (GEO)
Make sure AI tools describe your candidate or organization accurately and completely when voters, reporters, and funders ask questions.
What this playbook covers
Generative Engine Optimization is the practice of making public information clear, well-sourced, and structured so AI systems describe a candidate or organization accurately. It is about being the answer, not gaming a ranking.
Workflow steps
Step 1: Audit how AI currently describes you
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 2: Identify the questions AI is answering and the ones it should be
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 3: Improve the public sources AI relies on
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 4: Publish clear, citation-ready answers
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 5: Check political-specific source patterns
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 6: Build a review and correction process
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Step 7: Set an ongoing monitoring cadence
Complete this step with the relevant source material, document the decision, and keep a responsible person in the review loop before moving forward.
Quality and oversight
Verify factual claims against primary sources, respect data and tool policies, and use AI to support—not replace—human judgment, approval, and accountability.