¿Alguien más ha lidiado con alucinaciones de IA difundiendo información falsa sobre su marca? Acabo de descubrir que ChatGPT está inventando características de productos

You’re not overreacting. This is a real and documented problem.

The technical reality:

AI hallucinations occur because LLMs are fundamentally prediction machines, not truth machines. They predict the most statistically likely next token based on patterns in training data. When they encounter gaps or conflicting information, they fill those gaps with plausible-sounding content.

The numbers are sobering:

• ChatGPT hallucination rate: ~12% of responses
• Claude: ~15%
• Perplexity: ~3.3% (lower due to RAG)
• Global business losses from AI hallucinations in 2024: $67.4 billion

For lesser-known brands, hallucination rates can be even higher because there’s less training data to ground responses.

What you can do:

1. Improve your digital footprint - More accurate, structured content across authoritative sources gives AI systems better data to work with

2. Focus on high-authority platforms - Wikipedia, industry publications, and established review sites carry more weight in training data

3. Monitor continuously - Hallucinations change as models update. What’s wrong today might be right next month (or vice versa)

The situation isn’t hopeless, but it requires sustained effort.

We’re in healthcare tech, so AI hallucinations aren’t just a reputation issue - they’re potentially a compliance and safety issue.

Our nightmare scenario came true last year:

ChatGPT told a prospect that our patient management system “doesn’t meet HIPAA requirements.” We’ve been HIPAA compliant since day one. Had to have our legal team reach out to the prospect with certification documentation.

What actually helped us reduce hallucinations:

1. Structured FAQ pages - We created comprehensive FAQ pages with schema markup answering every conceivable question about our compliance, features, and capabilities

2. Third-party validation - Got our compliance certifications mentioned on G2, Capterra, and industry publications. AI systems seem to weight third-party sources heavily

3. Consistent messaging everywhere - Made sure our website, press releases, LinkedIn, and every other channel had identical, accurate information

4. Am I Cited monitoring - Started tracking AI mentions weekly. When we see hallucinations, we can trace back to potential source issues and address them

After 6 months of this work, the HIPAA hallucination disappeared. Still get occasional errors on other things, but the critical compliance stuff is now accurate across ChatGPT and Perplexity.

Small company perspective here - this is actually terrifying for startups.

We have limited content out there about our brand. Every piece of training data matters. And we’ve found that ChatGPT basically invents our feature set based on what competitors offer.

It’s like AI is playing “mad libs” with our product - “This company probably has [feature that competitors have]” becomes stated as fact.

The worst hallucination we found: ChatGPT said we were “acquired by [major competitor] in 2024.” We’re very much still independent. No idea where that came from.

Now I’m paranoid that prospects are disqualifying us based on completely fabricated information before ever visiting our website.

Coming at this from an SEO background - we’ve been dealing with featured snippet accuracy issues for years. AI hallucinations are that problem amplified 10x.

The key insight I’ve learned:

AI systems pull from the same content pool as Google, but they SYNTHESIZE rather than quote directly. This means small errors in your content can become large errors in AI responses.

Practical steps that help:

1. Audit your own content first - Sometimes AI hallucinations trace back to outdated blog posts, old press releases, or inconsistent information on your own site

2. Check what’s ranking for your brand queries - If inaccurate third-party content ranks well for “[your brand] features” or “[your brand] pricing,” that’s likely feeding AI training data

3. Build citation-worthy content - Perplexity specifically uses RAG (retrieval-augmented generation) and cites sources. If your content is structured well, it gets cited directly instead of hallucinated

4. Track the specific hallucinations - Document exactly what’s wrong, test across multiple AI platforms, and monitor whether it changes over time

The structured data approach mentioned above is huge. AI systems parse structured content better than dense paragraphs.

At enterprise scale, we’ve started treating AI hallucination monitoring as part of our standard brand health metrics.

Our approach:

We run quarterly “AI brand audits” where we test 50+ prompts across ChatGPT, Claude, Perplexity, and Google AI Overviews. Each response is scored for accuracy against our official product documentation.

Current stats from our last audit:

• ChatGPT accuracy on our brand: 73%
• Claude: 71%
• Perplexity: 89%
• Google AI Overviews: 82%

The Perplexity number is notably better because it uses live search and cites sources. The others are working from training data that’s months or years old.

What surprised us:

Some hallucinations were actually based on OLD but accurate information. Our pricing changed 18 months ago, and ChatGPT still has the old pricing. That’s not really hallucination - it’s outdated training data. But the effect on prospects is the same.

Journalist here who writes about AI. I’ve been tracking AI accuracy issues for a year now.

Something most people don’t realize:

AI hallucinations aren’t random. They follow patterns based on what’s in training data. If there’s conflicting information about your company online, AI will sometimes “average” between sources, creating hybrid facts that are partly true and partly invented.

Example I documented:

Company A acquired Company B’s product line in 2023. AI now sometimes attributes Company B’s features to Company A, and vice versa. The models are conflating two separate products because acquisition news mentioned both together.

For the OP:

The pricing hallucination ($99 vs $29) might trace back to an old pricing page, a competitor with similar pricing, or even a third-party comparison that had wrong info. Worth investigating the source.

We manage AI visibility for 30+ clients. AI hallucinations are now the #1 issue clients bring to us.

The framework we use:

1. Baseline Audit - Test 20-30 prompts across all major AI platforms, document every inaccuracy

2. Source Analysis - For each hallucination, try to trace where the false info might have originated (old content, competitor confusion, third-party errors)

3. Content Remediation - Create or update authoritative content that directly contradicts the hallucination with clear, structured information

4. Third-party Amplification - Get accurate information published on high-authority sites that AI systems weight heavily

5. Monitoring - Use Am I Cited to track AI mentions weekly. Hallucinations often self-correct when AI models update, but new ones can appear

Timeline reality check:

Fixing AI hallucinations is not fast. Expect 3-6 months for significant improvement. Training data doesn’t update instantly, and even RAG systems need time to discover and prioritize your corrected content.

Adding a legal perspective since this came up:

The current legal landscape:

There’s no established legal framework for holding AI companies liable for hallucinations. We’ve researched this extensively. While defamation and false advertising laws exist, applying them to AI-generated content is legally murky.

That said:

Some companies are exploring claims around tortious interference (when AI hallucinations demonstrably cause lost deals) and violations of state consumer protection laws. But these are untested theories.

Practical advice:

Document everything. If a prospect explicitly tells you they rejected your product based on AI misinformation, get that in writing. If this ever becomes actionable, you’ll want evidence of actual damages.

For now, the most effective remedy is proactive content strategy rather than legal action.

This thread has been incredibly helpful. Thank you all.

My takeaways and next steps:

1. This is a real, documented problem - Not overreacting. The numbers (12% hallucination rate, $67B in damages) validate my concerns

2. Source investigation first - Going to audit our own content and check what third-party content ranks for our brand queries

3. Structured content matters - Will work with our content team on FAQ pages with schema markup

4. Third-party validation - Need to get accurate info on G2, Capterra, and industry publications

5. Monitoring is essential - Setting up Am I Cited to track AI mentions. Can’t fix what we don’t measure

6. Patience required - 3-6 month timeline for meaningful improvement is good to know

Immediate action:

Reaching back out to that prospect with our actual certifications and feature list. Might not win them back, but at least they’ll know the truth.

The “reputation management on nightmare mode” comment was emotional, but honestly, it’s not unsolvable. Just requires a different approach than traditional brand management.