Opportunity ledger / Dossier O-0349

Corroborated▼ FadingOPPORTUNITY DOSSIER · O-0349

AI Hallucination Detection and Mitigation

Real-time detection and flagging of hallucinated content in AI responses, helping users avoid misinformation risks when frequently using AI.

First seen 2026-07-16 · Last updated 2026-10-16 · Recalculated daily

36.5
Evidence confidence, not a return forecast
5
Independent source items, deduplicated by post
3
Public source families
1
Payment evidence: current spend or explicit intent
ProblemUsers frequently encounter hallucination problems in all mainstream AI models, with hallucinations occurring on average once every 3-5 prompts, leading to decreased trust in AI outputs.
People affectedContent creators, researchers, and knowledge workers who frequently use AI tools like ChatGPT/Claude
Named alternativesClaude, Gemini and GPT, Gemini, GPT, Google AI
Topicsai hallucination mitigationcontent credibility verificationai usage efficiency
Collected sample dates · 12 weeks
07-2708-2409-2110-12

These observations describe collected samples, not platform-wide prevalence; they cannot establish whether a problem is widespread or accelerating. Collection scope changed on 09-16 and 09-27; monthly counts must not be read as trends.

Representative evidence

2 public excerpts · 5 items in the full chain
Product complaint★★★★☆Current spend

Post title: Does your Claude constantly lie and fake code results?

I've had a Claude Pro/Business account for two mon…

The public layer keeps only a minimal excerpt. Open the source for full context.

Reddit2026-07-21View source ↗
Product complaint★★★★☆First-hand pain

Post title: Why does A.I suddenly seem awful at science? and are there decent A.I for science questions/research

Gemini is just incorrect most the time and has zer…

The public layer keeps only a minimal excerpt. Open the source for full context.

Reddit2026-09-12View source ↗
★★★★☆
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What this evidence does not show yet

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Scoring summary

Evidence confidence = strength × evidence volume × source diversity × payment × competition × trend × 10. Counts use independent content items; only first-hand pain, current spend, explicit willingness to pay and concrete feature requests affect the score. Repeated posts by one author are discounted. Scoring and status changes follow deterministic rules.

“Payment-backed” means first-hand payment evidence exists in the record. It does not mean the business is worth building. “Fading” is a recency tag shown alongside any evidence level, not a lower level. Read the full methodology.