AI Hallucination Risk Calculator
Calculate the likelihood of AI hallucinations based on task type, model configuration, RAG status, and prompt engineering. Get actionable recommendations to reduce fabrication risk in your LLM applications.
Task Configuration
Model Configuration
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How Likely Is Your AI to Hallucinate?
AI hallucination—when models generate plausible-sounding but factually incorrect information—is one of the biggest challenges in deploying LLMs. Research shows hallucination rates vary dramatically based on task type, model size, temperature settings, and whether retrieval-augmented generation (RAG) is used. Our calculator estimates hallucination risk based on peer-reviewed research factors.
Understanding LLM Hallucination Risk
Hallucination risk depends on multiple factors: task type (factual Q&A has higher risk than creative writing), domain specificity (niche topics see more fabrication), model configuration (temperature, size), and mitigation strategies (RAG, prompt engineering). This calculator combines these factors using weighted risk modeling.
Risk Calculation
Risk = Σ(Factor × Weight) × (1 - RAG Reduction)Why Assess Hallucination Risk?
Deployment Decisions
High-risk use cases (medical, legal, financial) require more guardrails. Know your risk before going to production.
Configuration Optimization
Small changes in temperature or prompting can significantly reduce hallucination rates without sacrificing quality.
RAG Investment Justification
RAG implementation is expensive. Quantify the risk reduction to justify the engineering investment.
User Trust Management
Set appropriate user expectations. High-risk outputs need verification disclaimers and human review.
How to Use This Calculator
Frequently Asked Questions
Hallucinations occur because LLMs are trained to generate plausible text, not verify factual accuracy. They have no mechanism to distinguish what they 'know' from what they're generating. Pre-training data gaps, compression during training, and the probabilistic nature of token prediction all contribute. Recent research shows hallucination is an inherent property of LLMs, not a bug to be fixed.