Generative engine optimization report
Free generative engine optimization report template with prompt observations, citation gaps, entity coverage, and an action plan in editable XLSX and CSV.
Original, synthetic sample data only. Every figure in this asset is a worked example — replace each sample value with your own verified analytics before sending a client report.
What the free GEO report template covers
A generative engine optimization report explains how a site shows up inside AI-generated answers rather than only in the blue-link results. When someone asks a question and an AI engine returns a synthesized response, the report records whether your client's content was used, which sources were cited, and where a competitor or a gap got the mention instead. It exists because ranking on a results page and being quoted in a generated answer are now two different outcomes, and clients are starting to ask about both.
The download is an original editable XLSX workbook plus a companion CSV with synthetic observation rows. It is written for agencies and freelance consultants who already deliver search reporting and need to add an AI-answer view without inventing a whole new practice. Replace every sample with your own dated evidence before presenting it to a client.
How the GEO report is organized
The workbook is split into five tabs that move from observation to decision, so a long review still ends in a short list the client can act on. The CSV uses the same observation fields for teams that prefer a database, script, or spreadsheet import. Both files deliberately avoid a proprietary visibility score: the report shows what was observed, the evidence behind it, and the action that follows.
- Executive summary: prompt coverage, owned citations, competitor citations, confidence limits, and the decision requested from the client.
- AI answer observations: one dated row per prompt, engine, market, and persona with mention and citation evidence.
- Citation gaps: the owned page that should be eligible, the competing source currently cited, and the proof or format missing from the owned page.
- Entity coverage: whether core organization, product, author, location, and methodology facts are explicit and corroborated.
- Action plan: prioritized changes with an owner, due date, status, and expected evidence.
Field map for the report
Every row in the report uses the same fields so coverage can be sorted, compared across runs, and triaged into a plan. The map below explains what each column is for and how to fill it from your own tests rather than from guesses.
| Field | Purpose | How to use it |
|---|---|---|
| Prompt set / prompt | Keeps buyer questions grouped and repeatable across reporting periods. | Use stable prompt wording and one row per engine, market, and persona. |
| Engine / observed at | Identifies the sampled answer and when it was collected. | Record the exact engine label and ISO date; treat the result as an observation, not a rank. |
| Brand mentioned / owned URL cited | Separates a brand mention from a verifiable citation to an owned page. | Use yes/no values and paste the cited owned URL only when the answer exposes one. |
| Competitor citations | Shows which competing sources earned the citation instead. | List the domains or URLs visible in the answer; leave blank when none are shown. |
| Citation coverage / confidence | Provides a transparent roll-up and records evidence quality. | Use the workbook definitions; do not invent a universal AI visibility score. |
| Entity gap / next action | Turns an observed absence into a specific, owned improvement. | Name the missing proof or entity signal, then assign one page-level action and owner. |
| Evidence URL / notes | Keeps each conclusion reviewable after the answer changes. | Store a share link or internal evidence reference without copying private client data into public files. |
How to use the XLSX and CSV
Work from a fixed question set outward, not from whatever query you happen to think of on the day. Decide the questions a real buyer would ask in the client's category first, record them once, and reuse the same set every month so changes mean something. Then run each question through the AI engines in scope and log what you actually observed, not what you hoped to see.
Start in the XLSX when a consultant needs a client-facing summary and action plan. Start from the CSV when observations will be collected by several people or imported into another workflow. The field names match, so reviewed CSV rows can be pasted into the workbook without translating the evidence model.
Resist the urge to fix pages while you are still observing. The value of the report is the gap analysis at the end, where you compare where the client was cited against where it should be, and turn the difference into a small number of page priorities. An AI visibility checklist generator can help with page-level checks once those priorities are set, but the sequencing decision belongs in this report.
- Lock a question set that reflects real buyer intent, and keep it stable across runs.
- Test each question across the engines in scope and record coverage as appeared, cited, or absent.
- Note the cited source for every answer, so you can tell client assets from competitor and third-party ones.
- Convert the gaps into a ranked next-page list, each row carrying a reason and an owner.
Checks before you send it to a client
A GEO report loses trust quickly if it reads like a one-time screenshot of an AI tool. Because generated answers vary between runs and engines, every coverage claim should say when it was observed and which engine produced it, so the client understands you are reporting a sample over time rather than a fixed ranking. Before delivery, confirm that each entity gap points to a concrete page or fact you could add, and that the priority list names only a handful of moves rather than everything at once.
- Each coverage row records the engine and the date it was observed, since answers change between runs.
- Every entity gap maps to a specific page to build or a fact to add, not a vague theme.
- No invented metrics: leave any volume, difficulty, or traffic cells blank unless the number comes from a verified export.
- The summary names the few next-page priorities the client should approve, with owners attached.
FAQ
Generative engine optimization report FAQ
What is a generative engine optimization report?
It is a recurring deliverable that records how a site appears inside AI-generated answers, not just in standard search results. For a fixed set of questions, it captures whether the client was cited, which sources the engine used, and where the gaps are. It then turns those gaps into a ranked list of pages to build or strengthen next.
How is a GEO report different from a normal SEO report?
A normal SEO report explains ranking and traffic movement for pages on a results page, usually from your own verified Search Console and analytics exports. A GEO report focuses on whether AI engines quote the client's content when they synthesize an answer, which is a separate outcome. Many consultants run both: the SEO report for link visibility and this one for answer-level visibility.
Where do the numbers in a GEO report come from?
From your own observations, not from invented figures. You log each engine's answer to your question set and record coverage qualitatively as appeared, cited, or absent, with the date and engine noted. The template deliberately leaves metric cells blank unless a number comes from a source you verified, so the report stays honest about what you actually saw.
How often should I run a generative engine optimization report?
Monthly is a reasonable default for most retained clients, using the same question set each time so changes are comparable. Because generated answers vary between runs, treat each report as a sample over time rather than a fixed position. If you also maintain an AI search visibility checklist, run the two on the same cadence so the page-level checks and the answer-level coverage stay in sync.
Can I use this report as a client deliverable?
Yes. The structure and any sample rows are original and synthetic, so you can adapt them freely for paid engagements. Replace the samples with your own observed coverage, keep the engine and date noted on every row, and remove anything you could not confirm before sending it.