Project logic
Problem, system, output.
The system turns known SEO inputs into a governed workbook so prompt preparation can be repeated, reviewed, and explained.
Prompt sets drift when they are assembled by hand.
Manual prompt creation makes it difficult to preserve taxonomy logic, market nuance, category coverage, and brand context across repeated GEO research runs.
Workbook inputs become governed prompt operations.
The workflow combines taxonomy templates, keyword research, brand context, and QA checks into deterministic prompt rows that can be inspected before use.
A local prompt workbook with review points.
The handoff is an auditable workbook, not a black-box generation step. No external APIs are called, and sensitive source data stays on the machine.
Pipeline
From source inputs to reviewed output.
- Input
Taxonomy
Category and intent structure anchors each generated row.
- Input
Keyword bank
Synthetic keyword tokens create a demand layer without exposing source research.
- Rules
Context rules
Brand, wording, market, and research constraints are applied before generation.
- Logic
Deterministic generation
Templates and variables create repeatable prompt rows.
- Review
QA layer
Shortfalls, duplicates, assumptions, and review needs are flagged.
- Output
Export workbook
The output remains inspectable before any research use.
Artifact proof
Two public-safe specimens.
Synthetic examples only. No private source material is shown.
Workbook specimen
Synthetic taxonomy workbook preview
A simplified view of how source structure becomes workbook logic without exposing real taxonomy or keyword data.
| Tab | Field | Synthetic example |
|---|---|---|
| Taxonomy | Category group | Synthetic category A |
| Keywords | Keyword token | Synthetic keyword A |
| Context rules | Neutral rule | Use public-safe wording |
| QA | Review flag | Check coverage before export |
Prompt anatomy
Prompt row anatomy
Each row is treated as an auditable record with a template, variable, keyword token, QA note, and final output row.
| Part | Synthetic value | Purpose |
|---|---|---|
| Template | Question pattern with one variable | Keeps structure consistent |
| Variable | Synthetic category A | Keeps taxonomy traceable |
| Keyword | Synthetic keyword A | Keeps the example public-safe |
| QA note | Review assumption before use | Keeps judgement in the loop |
Mini representation
Build one prompt row
Choose a synthetic category, keyword token, and neutral context to see how a public-safe prompt row is assembled.
- Template
- Question pattern with category, keyword token, and context rule
- Final row
- Research how generated answers describe Synthetic category A when the query uses Synthetic keyword A and the context rule is neutral.
- QA note
- Check assumptions before export. Synthetic row only.
Simplified portfolio representation. Synthetic values only; no external API calls.
Governance / QA
Why repeatability matters in GEO research
GEO research is only useful if the prompt set can be trusted. A repeatable local workflow makes it easier to compare markets, explain assumptions, re-run research, and separate preparation from any later visibility measurement.
- Deterministic generation keeps prompt preparation consistent between runs.
- Local processing protects sensitive taxonomy, keyword, and brand inputs.
- QA flags create a human review layer before prompts become research evidence.
What this demonstrates
- GEO and AI-search operations thinking.
- Prompt taxonomy design.
- SEO/content workflow automation.
- Governance around emerging search research.
- Working carefully with messy spreadsheet inputs.
Next iterations
- Custom brand context packs.
- Stronger template-variable mapping.
- A public demo dataset.
- Export presets for different research workflows.
- Optional LLM visibility measurement once the research method is defined.