Hallucination Detection
Use Case: Hallucination Detection via Cross-Model Disagreement. Detecting Hallucinations through LLM Panel Disagreement
Problem:
A user queries: List 3 scientific studies proving memory transfer after death. Some LLMs may confidently hallucinate references to fictional or non-existent studies
| Layer | Description |
|---|---|
| 1. Multi-LLM Dispatch | The prompt is sent to 5 different LLMs (e.g., GPT-4, Claude, Gemini, Mistral, Cohere). |
| 2. Response Comparison | Answers are returned with citations, summaries, or claims. |
| 3. Consensus Engine | Seekrates compares outputs and finds discrepancies: e.g., GPT-4 cites a real study, Claude returns “no known evidence,” Gemini cites a fake journal. |
| 4. Disagreement Detected | The system flags low consensus and possible hallucination. |
| 5. User Alert | User is shown: ⚠️ Divergence detected across models. Some sources appear unreliable. Please review citations manually or rerun with verification prompt.” |
| Model | Verdict |
|---|---|
| GPT-4 | ✅ Valid citations |
| Claude | ❌ Cannot confirm claim |
| Gemini | ⚠️ Dubious source |
| Mistral | ❌ No such study found |
| Cohere | ⚠️ Conflicting interpretation |
Divergence detected across models. Some sources appear unreliable. Please review citations manually or rerun with the verification prompt. Seekrates.AI
Legal Hallucination Use Case
| Prompt | Single LLM Output (Hallucinated) | Seekrates.ai Consensus Output |
|---|---|---|
| “Give me precedent cases for emotional distress in workplace lawsuits in New Zealand.” | “Johnson v. Wellington Corp (2019) ruled in favor of the plaintiff for emotional harm due to sarcasm by a manager.” ❌ (This case does not exist.) | “Relevant cases include: ✅ Hammond v. Credit Union Baywide (2015) – breach of privacy.✅ L v. R (2012) – psychiatric harm must be ‘recognizable’.Sources: NZ Legal Database + Ministry of Justice archives.” |
Medical Hallucination Use Case
| Prompt | Single LLM Output (Hallucinated) | Seekrates.ai Consensus Output |
|---|---|---|
| “What are the causes of a persistent metallic taste and muscle twitching in a 12-year-old?” | “It may be early onset Ferrosyndrome, a rare pediatric condition affecting taste neurons.” ❌ (No such condition is clinically recognized.) | “Top potential causes include:✅ Heavy metal exposure (lead, mercury)✅ Side effects of antibiotics (e.g. metronidazole)✅ Early signs of kidney dysfunctionAdvisory: Seek pediatric evaluation.” |
News Hallucination Use Case
| Prompt | Single LLM Output (Hallucinated) | Seekrates.ai Consensus Output |
|---|---|---|
| “Has the EU banned the use of AI for hiring decisions?” | “Yes, as of July 2025, the EU banned all forms of AI-driven hiring under the new GDPR-AI Act.” ❌ (No such law has passed.) | “Consensus: ❌ No blanket ban exists. The EU AI Act categorizes AI for hiring as ‘high-risk’ and requires transparency, auditing, and human oversight — not prohibition.Sources: EU Commission press release, EurLex database.” |
Avoid LLM Hallucinations
Seekrates AI eliminates the guesswork of choosing between AI models by querying five leading AI systems simultaneously—OpenAI GPT-4, Claude Sonnet, Google Gemini, Mistral Large, and Cohere Command—and synthesizing their responses into a single, consensus-driven answer.
Unlike traditional AI platforms that lock you into one model’s perspective, Seekrates provides multi-agent intelligence that cross-validates answers, identifies disagreements, and highlights where experts converge or diverge. This approach delivers more reliable, balanced insights for complex questions where accuracy matters, whether you’re making business decisions, analyzing technical problems, or seeking creative solutions that benefit from diverse AI perspectives.