Best Practices to Mitigate Negative Sentiment in AI Answers (2025)

If a prospective customer asks an AI assistant about your brand in 2025, the answer they see—its tone, sources, and accuracy—often matters as much as a traditional search result. Usage keeps climbing: in June 2025, the share of U.S. adults who have used ChatGPT roughly doubled vs. 2023, according to the Pew Research Center 2025 short read on ChatGPT usage. And AI is blending into everyday behaviors—“27% of U.S. adults say they interact with AI almost constantly or frequently,” per the Pew Research Center April 2025 report ‘Artificial Intelligence in Daily Life’.

Google’s AI Overviews/AI Mode are also material: a March 2025 study found AI Overviews triggered on 13.14% of all queries (88.1% informational), per the Semrush 2025 AI Overviews study. If AI-generated answers feature negative citations or skew negative in sentiment, your brand perception and click-throughs can suffer.

This article shares field-tested best practices to detect, triage, and remediate negative AI citations and sentiment—quickly and repeatably—across ChatGPT, Perplexity, and Google AI Overviews. Where useful, I’ll show how to operationalize these steps with Geneo, an AI-era brand visibility and sentiment platform across answer engines.

1) Detect early, triage fast: the always-on monitoring foundation

In practice, reputation wins are decided within hours, not weeks. The first layer is continuous, cross-platform listening with clear thresholds and playbooks.

What to implement now

• Cross-answer-engine monitoring. Track brand mentions and citations across ChatGPT, Perplexity, and Google AI Overviews. Monitor both the presence of your brand and the sources AIs cite alongside it.
• Sentiment scoring and deltas. Watch not only overall sentiment but directional change by platform, topic, and geography. A sudden shift toward negative on one engine is an early smoke signal.
• Negative-citation capture. Log the exact passages and URLs AIs are citing. Persist the “answer snapshot” (question + AI output + sources) to compare over time.
• Triage taxonomy. Classify incidents (inaccurate fact, outdated info, unfair framing, harmful allegation, privacy-sensitive, IP misuse). Attach severity and potential impact.
• SLAs and ownership. Define who responds within what timeframe per severity. For example: P1 privacy/defamation candidates: triage within 1 hour; P2 factual inaccuracy: 24 hours.

KPIs to track

• Mean time to detect (MTTD) negative citations by platform
• Mean time to respond (MTTR) with a correction or mitigation action
• Share of neutral/positive answers after 30/60/90 days
• Acceptance rate of corrections (where platforms provide mechanisms) and number of attempts

Applied with Geneo

• Use Geneo to centralize cross-platform brand monitoring and real-time sentiment analysis. Configure alerting thresholds for negative spikes; capture and tag snapshots for each incident. Geneo’s historical query tracking makes it straightforward to compare answers and sentiment before and after remediation across weeks or months.

2) Platform-by-platform correction workflows that actually get traction

Different engines offer different levers. Here’s what I’ve found effective, with the exact channels you’ll need.

A) Google AI Overviews / AI Mode

Your two levers are publisher-side quality signals and user-side feedback.

1. Improve the underlying page(s) that should be cited

• Align with Google’s 2025 guidance for AI experiences: unique, people-first answers to specific questions; accessible content (HTTP 200, not blocked); structured data; and freshness, as laid out in Google Search Central’s May 2025 ‘Top ways to ensure your content performs well in Google’s AI experiences on Search’.
• Implement schema where appropriate (Organization, Product, FAQ, HowTo). Ensure clean HTML, canonical tags, and consistent identity signals, per Google’s AI features documentation (2025).
• Publish a precise, well-cited explainer addressing the exact question that triggers the negative overview. Include primary sources, dates, and clear claims that are easy to quote.

2. Submit in-product feedback when the overview is wrong or unfair

• At the bottom of an AI Overview, use thumbs down, then “Report a problem,” select the best category, add context, and submit—steps described in Google Support’s ‘Give feedback on AI Overviews’ (2025). Encourage employees and customers impacted by the misinformation to do the same (without brigading or scripted text).

3. Revisit blocking settings intentionally

• If you explicitly block snippets/noindex, AI features may not surface your content. Confirm your intent based on the trade-offs explained in Google’s AI features documentation (2025).

Field note: You don’t “force” inclusion in AI Overviews; you earn it via helpful, reliable content and clear entities. Feedback helps flag issues; content quality makes fixes stick.

B) OpenAI ChatGPT

1. Use the product’s feedback loop on problematic answers

• For individual answers, use thumbs-down/“report” and include precise correction language and citations. This is the fastest signal you can send into the system, per the general process referenced in the OpenAI Help Center (2025).

2. Engage official channels for sensitive corrections

• Privacy or personal data issues: follow the process in the OpenAI Privacy Policy (ROW) (2025) to request corrections/removal where applicable.
• OpenAI states that feedback helps improve future versions and model behavior; see the OpenAI Model Spec (2025) for how the system is intended to respond to feedback and safety constraints.

3. Reinforce with high-quality, up-to-date source content

• Publish authoritative, neutral articles that are easy for assistants to quote. Use explicit dates, named experts, and clear definitions. Avoid promotional tone; assistants favor helpful, well-cited sources.

C) Perplexity

1. Submit bug reports and issues

• Perplexity’s formal path for issues is email-based; share the prompt, the answer, and why it’s wrong with sources, per the Perplexity Help Center ‘Where do I report bugs?’ (2025).

2. Understand its citation behavior

• Perplexity emphasizes real-time search and linked citations in answers (see the Perplexity ‘Getting started’ product blog, 2025). If your content is not being surfaced, examine findability (crawlability), specificity, and authority of your sources.

3. Strengthen the page that should be cited

• Ensure topic-matching pages with clear titles, scannable structure, succinct answers, and credible references. If third-party reviews or standards bodies cover your claims, cite them.

Applied with Geneo

• Track which platform(s) show the negative answer, the exact sources being cited, and whether each correction step was executed. Use Geneo’s multi-platform logs to coordinate owner, timestamp, and outcome across teams, then compare answer snapshots over time.

3) Proactive entity and content hygiene: make the “right” answer easy to cite

I’ve found most persistent negative answers trace back to one or more of these root causes: stale or missing canonical pages, inconsistent entity signals, or weak third-party corroboration.

Build a durable foundation

• Canonical explainers: Maintain definitive, dated explainers for contentious topics—policies, product limitations, safety practices, pricing definitions, and methodology. Put the answer in the first two paragraphs.
• Structured data: Add appropriate Organization, Product, FAQ, and HowTo schema so parsers understand page purpose. Keep HTML clean and accessible.
• Entity consistency: Align brand name, domain, social handles, logo, and descriptors everywhere. Inconsistent identity confuses both users and machines.
• Wikipedia/Wikidata: If your entity is notable, avoid undisclosed COI editing. Propose neutral, sourced updates on Talk pages with reliable third-party citations, per Wikipedia’s guidance on conflict-of-interest editing (policy pages current in 2025). This often stabilizes how assistants summarize you.

Applied with Geneo

• Geneo’s optimization suggestions can highlight thin coverage areas (e.g., missing canonical Q&A pages) and help prioritize which pages to improve first based on observed AI answers and sentiment trends.

4) Seed positive, verifiable narratives (without astroturfing)

Assistants reward clarity, credibility, and corroboration. You’re not “PR’ing” the model; you’re making it easy for the system to pick accurate, high-signal sources.

What typically works

• Primary data: Publish data cuts, benchmarks, or audits you can stand behind (methodology, date, sample). Assistants tend to cite primary artifacts.
• Independent corroboration: Earn coverage or references from reputable third parties (standards bodies, academic collaborators, respected trade media). One top-tier corroboration can outweigh dozens of low-quality mentions.
• FAQs and contradictions: Maintain a living FAQ that directly addresses common misconceptions and past errors. Mark updates with dates and link to sources.
• Neutral tone: Avoid hype. Make claims falsifiable and sourced. Assistants surface helpful, non-promotional explanations.

Field evidence: Differences in guardrails and behavior across chatbots have been documented, underscoring the need for robust, multi-pronged corrections and trustworthy sources, as shown in the Harvard Misinformation Review study auditing LLM chatbots on disinformation (2024–2025 discussion).

5) Crisis playbook: thresholds, escalations, and compliance

When a negative AI answer crosses into privacy, safety, or defamation territory, speed and documentation matter.

Operational guardrails

• Severity definitions: P1 (privacy/defamation/safety risk), P2 (material factual inaccuracy), P3 (framing/tone concern). Align on examples.
• SLAs: P1: detect → triage within 1 hour; initiate platform and legal processes within 4 hours; publish corrective statement same day. P2: 24-hour response; P3: 72-hour response.
• Evidence package: Keep prompt, answer text, screenshots, timestamps, and the URLs cited. Include your corrected copy with sources.
• Legal/compliance: In the EU, transparency and provider obligations are evolving under the EU AI Act (entered into force 2024; GPAI transparency from Aug 2, 2025). Track provider channels and document requests, per the European Parliament overview of the EU AI Act (2025 update).

Applied with Geneo

• Use Geneo to centralize incident records (answer snapshots, sources, timestamps), assign owners, and track status across legal, comms, and product. Historical views help demonstrate due diligence over time.

6) Measurement: prove the fix and institutionalize the loop

Anecdotes don’t scale. Instrument your program with clear milestones.

30/60/90-day outcome goals

• 30 days: Reduce MTTD and MTTR by 30–50% from baseline; stabilize P1 incidents with clear playbooks.
• 60 days: Achieve net-neutral or better sentiment across your top 25 question clusters on each engine.
• 90 days: Increase share of target pages cited in AI answers for priority queries; cut recurring inaccuracies by half.

Core dashboards

• Incident funnel: detections → triaged → corrected → verified → recurrences
• Sentiment by engine and topic cluster, including deltas week-over-week
• Citation mix: which of your pages (and trusted third parties) are being cited; gaps vs. target list

Applied with Geneo

• Geneo’s historical query tracking visualizes answer and sentiment changes over time. Combine with its content optimization suggestions to run controlled improvements (e.g., structured data, updated definitions) and measure impact on citations and tone.

7) Workflow walkthrough: from negative citation to durable fix

Below is a practical sequence you can adapt. Replace the tool choices with your stack where needed; the logic holds.

1. Detect and capture

• Alert triggers on “Brand X data breach” queries show ChatGPT citing an outdated 2022 blog post implying ongoing risk. You capture the full answer, sources, and timestamp.

2. Triage and classify

• Classify as P2 (material factual inaccuracy): the issue is outdated, not malicious.

3. Correct the record at the source

• Publish a dated explainer: “Security incident resolved in 2022; current controls as of 2025,” with links to third-party audits. Add Organization and FAQ schema.

4. Submit platform feedback

• ChatGPT: thumbs-down on the problematic answer with your corrected summary and citations; follow up via the Help Center if the issue persists, per the OpenAI Help Center (2025).
• Google AI Overviews: report the problem and ensure your new page is crawlable and helpful, following Google Search Central’s May 2025 guidance.
• Perplexity: send a detailed issue email including the prompt, wrong answer, and your sources via the Perplexity Help Center route (2025).

5. Reinforce entity consistency

• Update your About/Press pages; ensure consistent naming and dates; if notable, propose neutral Wikipedia Talk-page updates with reliable sources, per Wikipedia’s COI guidance (2025).

6. Verify change and track recurrence

• Re-run the queries weekly for 8–12 weeks. Compare snapshots. If the error persists, iterate: add corroboration, simplify phrasing, or strengthen third-party evidence.

Applied with Geneo

• Do this workflow inside Geneo: the platform logs detections, hosts snapshots, assigns owners, and charts sentiment over time across ChatGPT, Perplexity, and AI Overviews. Use its optimization suggestions to prioritize which pages or entities to strengthen first.

Field case to study for inspiration

• One public example of entity correction comes from Kalicube. They report that engineered entity alignment reduced hallucinations and delivered business impact for a personal brand, as documented in the Kalicube case study on eliminating AI hallucinations (2024–2025). Your mileage will vary, but the approach—clear entities, consistent corroboration, and repeated verification—maps well to brand scenarios.

8) Common pitfalls that extend the damage window

• Treating AI answers like SERP snippets: AI answers often synthesize and frame; they’re not just lists of links. You must manage both tone and citations.
• Only filing feedback without fixing content: Feedback flags issues; it rarely sticks without authoritative, up-to-date pages to cite.
• Over-PR-ifying: Promotional tone gets suppressed. Write for helpfulness, clarity, and verifiability.
• Ignoring entity hygiene: Inconsistent names, logos, or descriptors ripple into answer ambiguity and negative associations.
• Single-platform myopia: A correction on one engine doesn’t propagate automatically. Triangulate across ChatGPT, Perplexity, and AI Overviews.
• No documentation: If a crisis escalates, you’ll need evidence and a paper trail—especially under regimes evolving like the EU AI Act overviewed by the European Parliament (2025).

9) Quick checklist: from detection to durability

Monitoring

• [ ] Cross-engine monitoring live (ChatGPT, Perplexity, Google AI Overviews)
• [ ] Sentiment scoring with weekly deltas
• [ ] Negative-citation snapshots captured and tagged

Triage & Response

• [ ] Severity taxonomy and SLAs agreed with Legal/Comms
• [ ] Owners and escalation paths defined
• [ ] Platform feedback steps documented (links handy)

Content & Entity

• [ ] Canonical explainers for contentious topics
• [ ] Structured data (Org/Product/FAQ/HowTo) validated
• [ ] Consistent identity signals across web properties
• [ ] Wikipedia Talk-page strategy (if notable), per Wikipedia COI guidance

Measurement

• [ ] MTTD and MTTR tracked by platform
• [ ] 30/60/90-day sentiment and citation goals defined
• [ ] Incident funnel reports reviewed weekly

Compliance

• [ ] Evidence package template for P1 incidents
• [ ] Tracking of requests to platforms and outcomes
• [ ] Awareness of provider obligations evolving under the EU AI Act (2025)

10) Where Geneo fits in your 2025 stack

Based on the workflows above, Geneo can streamline the heavy lifting across AI answer engines:

• Cross-platform monitoring: Real-time tracking of brand exposure, link citations, and mentions across ChatGPT, Perplexity, and Google AI Overviews from one dashboard.
• AI-driven sentiment analysis: See tone by engine and topic cluster, plus shifts over time.
• Historical query tracking: Capture and compare answer snapshots to validate that fixes stick.
• Content optimization suggestions: Prioritize which canonical pages and entities to strengthen to influence how AIs answer next time.
• Multi-team, multi-brand collaboration: Assign incidents, record actions, and standardize playbooks across legal, comms, and SEO.

If AI answers are part of how customers discover and judge your brand—and in 2025 they increasingly are—build this capability as a core, ongoing discipline. Start by getting visibility, then operationalize the fix cycle, and finally institutionalize measurement and continuous improvement.

Ready to see how this looks in practice? Explore Geneo’s platform and free trial at https://geneo.app.

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References cited inline:

• Usage growth and everyday AI interaction: Pew Research Center 2025 short read on ChatGPT usage and Pew Research Center 2025 report ‘Artificial Intelligence in Daily Life’
• AI Overviews prevalence: Semrush 2025 AI Overviews study
• Google guidance and feedback: Google Search Central May 2025 guidance, Google AI features docs, and Google Support feedback steps
• OpenAI processes: OpenAI Help Center, OpenAI Privacy Policy (ROW), OpenAI Model Spec
• Perplexity processes and behavior: Perplexity Help Center bug reporting, Perplexity ‘Getting started’
• Entity hygiene: Wikipedia COI guidance
• Regulation context: European Parliament overview of the EU AI Act
• Guardrails variability: Harvard Misinformation Review audit of LLM chatbots