How to Dispute Wrong Information in Llama

Step-by-step process for disputing and correcting inaccurate brand information in Llama.

Llama will confidently state your product costs $99 when you actually charge $149. It'll claim your CEO is someone else entirely. Unlike search engines, Llama doesn't show sources for its claims, making bad information harder to trace and harder to fix. Meta doesn't offer correction forms, but Llama learns from the web. Here's how to systematically dispute wrong information.

The Problem

Llama's training data is static until Meta updates it, which happens irregularly. When Llama encounters incomplete information during training, it fills gaps with confident-sounding guesses. These fabrications become 'facts' that millions of users accept without question.

The Solution

You can't email Meta to fix Llama's training data. But you can improve the web sources that feed future updates. The strategy is identifying what Llama gets wrong, tracking it back to source material, and systematically replacing bad information with authoritative, current facts across high-trust domains.

Test Llama systematically for wrong facts

Ask Llama specific questions: 'What is [your brand]?', 'How much does [product] cost?', 'Where is [company] headquartered?' Screenshot everything. Test different phrasings of the same question. Llama often gives consistent wrong answers, which means the error is baked into its training.

Trace errors back to their web sources

Google the exact phrases Llama uses. You'll find the sources: old press releases, outdated Wikipedia entries, competitor pages with wrong info. Sometimes Llama combines multiple sources into one confident but incorrect response. Knowing the origin tells you where to focus fixes.

Fix high-authority sources first

Start with Wikipedia, Crunchbase, and major news sites. Update Wikipedia with proper citations (follow their guidelines strictly). Contact Crunchbase support for corrections. Reach out to journalists who wrote articles with outdated information. These sources heavily influence AI training.

Strengthen your official web presence

Add explicit, unambiguous facts to your About page, pricing pages, and press kit. Use clear language: 'Founded in March 2021' not 'Recently established.' Add schema markup so AI can parse structured data. Include 'Last Updated' timestamps on key pages.

Publish content that directly contradicts errors

If Llama says you're based in San Francisco when you're in Austin, publish a blog post titled 'Why [Company] Chose Austin Over Silicon Valley.' Use the wrong information as a hook, then correct it explicitly. This creates new training material that directly addresses the misconception.

Document and track changes over time

Create a spreadsheet of every wrong fact you found. Test the same questions monthly and document changes. Llama's knowledge updates when Meta retrains the model, which happens unpredictably. Some corrections appear in months, others take over a year.

Build citation networks around correct facts

Get quoted in industry publications with your correct information. Speak at conferences where transcripts mention accurate details. The more authoritative sources that cite the same correct facts, the stronger the signal for future training data.