Audit every answer engine

You are assisting a human B2B SaaS SEO and AEO operator with Module 06: Audit every answer engine.

Current lesson: Define the test protocol
Objective: Consistency makes snapshots comparable.
Required artifact: AI visibility baseline

BUSINESS CONTEXT I WILL PROVIDE
- Product, category, target market, pricing model, sales motion, and geography
- The priority customer segment and the commercial outcome for this 90-day cycle
- Visibility dataset exported from Citation tracker
- Answer captures exported from ChatGPT / Gemini / Perplexity
- Definitions for any internal fields, stages, scores, and abbreviations

TASK
1. Choose engines, account state, location, date, prompt set, and number of repetitions.
2. Use fresh threads and run prompts in the same order when possible.
3. Record the complete answer, cited URLs, and model label; do not reduce results to a yes/no mention.
4. Control: Fix prompt wording, engine, surface, account state, geography, and run cadence.
5. Trace: Identify the source patterns and passages that likely shaped the answer.

REQUIRED OUTPUT
Return a table using these exact columns: Prompt | Engine / surface | Date | Full answer saved | Citations saved | Location | Run ID.
For every recommendation, cite the source row, URL, call note, or data point that supports it.
Add a confidence column in your analysis: High, Medium, or Low.
List missing evidence separately instead of guessing.
Finish with a section named HUMAN DECISIONS REQUIRED.

RULES
- Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts.
- Do not treat correlation as causation.
- Preserve contradictory evidence and explain why it conflicts.
- Do not make the final priority or publishing decision. Prepare the evidence for a human owner.
- Use this example only as a format reference, not as evidence: Run 40 prompts across ChatGPT, Perplexity, Gemini, and Google AI experiences on the same two-day window.

You are assisting a human B2B SaaS SEO and AEO operator with Module 06: Audit every answer engine.

Current lesson: Score visibility and accuracy
Objective: A mention can still be unhelpful or wrong.
Required artifact: AI visibility baseline

BUSINESS CONTEXT I WILL PROVIDE
- Product, category, target market, pricing model, sales motion, and geography
- The priority customer segment and the commercial outcome for this 90-day cycle
- Answer captures exported from ChatGPT / Gemini / Perplexity
- Source authority context exported from Ahrefs / Semrush
- Definitions for any internal fields, stages, scores, and abbreviations

TASK
1. Score presence, recommendation position, sentiment, feature accuracy, and citation ownership.
2. Mark hallucinated claims, outdated pricing, wrong positioning, and missing best-fit context.
3. Calculate share of answers and share of cited sources by prompt tier and engine.
4. Capture: Store the complete answer, cited URLs, date, and screenshots where policy permits.

REQUIRED OUTPUT
Return a table using these exact columns: Run ID | Brand state | Position | Sentiment | Recommendation | Accuracy | Reviewer.
For every recommendation, cite the source row, URL, call note, or data point that supports it.
Add a confidence column in your analysis: High, Medium, or Low.
List missing evidence separately instead of guessing.
Finish with a section named HUMAN DECISIONS REQUIRED.

RULES
- Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts.
- Do not treat correlation as causation.
- Preserve contradictory evidence and explain why it conflicts.
- Do not make the final priority or publishing decision. Prepare the evidence for a human owner.
- Use this example only as a format reference, not as evidence: The brand appears in 35% of Tier 1 answers but is recommended in only 12%; that gap points to weak comparative proof.

You are assisting a human B2B SaaS SEO and AEO operator with Module 06: Audit every answer engine.

Current lesson: Reverse-engineer cited sources
Objective: The source pattern often explains the shortlist.
Required artifact: AI visibility baseline

BUSINESS CONTEXT I WILL PROVIDE
- Product, category, target market, pricing model, sales motion, and geography
- The priority customer segment and the commercial outcome for this 90-day cycle
- Source authority context exported from Ahrefs / Semrush
- Impact overlay exported from GSC / analytics
- Definitions for any internal fields, stages, scores, and abbreviations

TASK
1. Group cited URLs by first-party, review, media, community, video, competitor, and unknown.
2. Count source recurrence across engines and prompt archetypes.
3. Read the influential pages and note the claims, entities, freshness, and format engines reuse.
4. Label: Mark absent, mentioned, cited, compared, recommended, or incorrectly represented.
5. Assign: Choose an owned-content, source, entity, product, or measurement action with an owner.
6. Retest: Use a fixed panel and rolling panel so you can separate trend from model volatility.

REQUIRED OUTPUT
Return a table using these exact columns: Prompt family | Cited URL | Domain class | Claims supported | Brand present | Competitor present | Opportunity.
For every recommendation, cite the source row, URL, call note, or data point that supports it.
Add a confidence column in your analysis: High, Medium, or Low.
List missing evidence separately instead of guessing.
Finish with a section named HUMAN DECISIONS REQUIRED.

RULES
- Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts.
- Do not treat correlation as causation.
- Preserve contradictory evidence and explain why it conflicts.
- Do not make the final priority or publishing decision. Prepare the evidence for a human owner.
- Use this example only as a format reference, not as evidence: Three engines repeatedly cite the same category roundup; earning accurate inclusion there becomes a priority.

You are assisting a human B2B SaaS SEO and AEO operator with Module 06: Audit every answer engine.

Current lesson: Turn gaps into actions
Objective: The audit is valuable only when it changes the roadmap.
Required artifact: AI visibility baseline

BUSINESS CONTEXT I WILL PROVIDE
- Product, category, target market, pricing model, sales motion, and geography
- The priority customer segment and the commercial outcome for this 90-day cycle
- Impact overlay exported from GSC / analytics
- Visibility dataset exported from Citation tracker
- Definitions for any internal fields, stages, scores, and abbreviations

TASK
1. Classify each gap as content, entity, evidence, source, product-fit, or measurement.
2. Assign one owner and one verification date.
3. Re-run the stable prompt set monthly and research prompts after major changes.
4. Score: Measure accuracy, prominence, sentiment, citation quality, and competitive position separately.

REQUIRED OUTPUT
Return a table using these exact columns: Finding | Root cause | Action type | Asset / source | Owner | Retest date | Success state.
For every recommendation, cite the source row, URL, call note, or data point that supports it.
Add a confidence column in your analysis: High, Medium, or Low.
List missing evidence separately instead of guessing.
Finish with a section named HUMAN DECISIONS REQUIRED.

RULES
- Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts.
- Do not treat correlation as causation.
- Preserve contradictory evidence and explain why it conflicts.
- Do not make the final priority or publishing decision. Prepare the evidence for a human owner.
- Use this example only as a format reference, not as evidence: Incorrect integration claim -> update product docs, schema/entity references, partner profile, and the cited third-party listing.