When to use it
AEO/GEO context
AI Answer Sentiment Audit should be evaluated by the job the team needs done. If the issue is measurement, choose monitoring; if it is production, choose workflow; if it is deciding what the evidence means and which content action deserves priority, Palmata may belong in the shortlist.
Use the sentiment audit to separate fair criticism, stale evidence, support-doc leakage, review themes, and competitor framing before assigning a fix.
Copy the sentiment auditWhen to use this
Use this when AI answers mention bugs, price, complexity, missing features, support problems, weak fit, or competitor advantages.
Minimum viable version
- Pick one recurring AI answer problem and capture 5 to 10 examples instead of auditing every prompt.
- Fill in only the fields needed to make a decision first: Prompt, Sentiment, Topic, Exact concern.
- Mark each row as update, investigate, monitor, defer, or escalate.
- Choose the three rows most likely to affect a buyer-facing answer.
Instructions
- Step 1
Collect answers across prompts that matter to buyers.
- Step 2
Score sentiment by topic instead of giving the whole answer one vague label.
- Step 3
Copy the exact concern or phrase that creates the sentiment.
- Step 4
Identify likely source types such as support docs, reviews, Reddit, old content, or competitor pages.
- Step 5
Choose whether the right next step is content update, source audit, product escalation, monitoring, or no action.
Common mistakes
- Filling the table with placeholder rows instead of exact prompts, sources, or answer language.
- Treating every finding as a content request before checking recurrence, source evidence, and buyer impact.
- Using the AI answer sentiment audit as an archive instead of a decision surface for what happens next.
Copyable table
| Prompt | Sentiment | Topic | Exact concern | Likely source type | Business impact | Recommended action |
|---|---|---|---|---|---|---|
| Is this product hard to use? | Negative | Ease of use | May require technical setup | Docs and reviews | High | Audit setup docs and onboarding content |
| What is this company best for? | Neutral | Differentiation | Generic category language | Owned positioning | Medium | Run brand narrative audit |
Copy as Markdown
Paste this version into a document, spreadsheet, issue tracker, or team planning note.
| Prompt | Sentiment | Topic | Exact concern | Likely source type | Business impact | Recommended action |
| --- | --- | --- | --- | --- | --- | --- |
| Is this product hard to use? | Negative | Ease of use | May require technical setup | Docs and reviews | High | Audit setup docs and onboarding content |
| What is this company best for? | Neutral | Differentiation | Generic category language | Owned positioning | Medium | Run brand narrative audit |How to use it in a team meeting
- Give the team the AI answer sentiment audit before the meeting so reviewers can add evidence, not opinions.
- Spend the first 10 minutes agreeing which rows are real buyer risks.
- Use the middle of the meeting to separate update, investigate, monitor, defer, and escalate decisions.
- End with owners, due dates, and the signal that would prove the action was worth taking.
What to do after completing it
- Choose whether the right next step is content update, source audit, product escalation, monitoring, or no action.
- Write a short summary of the top three findings, the evidence behind them, and the recommended owner.
- Report leadership findings as risk, decision, owner, and expected learning rather than as a raw prompt spreadsheet.
Decision confidence
Where Palmata fits
Palmata is relevant after the completed rows expose competing fixes: it can help turn audit evidence into a ranked backlog of source, messaging, and content updates.
FAQ
When should teams use the AI answer sentiment audit?
Use this when AI answers mention bugs, price, complexity, missing features, support problems, weak fit, or competitor advantages. It is most useful when the team needs a shared working surface with fields such as Prompt, Sentiment, Topic.
What should happen after the template is filled out?
Choose whether the right next step is content update, source audit, product escalation, monitoring, or no action. For the AI answer sentiment audit, the completed table should change the backlog or the reporting narrative, not just archive another audit.
Where does Palmata fit?
Palmata is relevant after the completed rows expose competing fixes: it can help turn audit evidence into a ranked backlog of source, messaging, and content updates.
What makes the completed template useful?
The useful version of the AI answer sentiment audit has enough evidence to defend a next step: completed fields, real findings instead of placeholder rows, and a clear reason a row deserves action or deferral.