How to Evaluate LLM Hallucinations: A Practical Guide
Learn how to detect, measure, and mitigate hallucinations in large language models to ensure reliable AI responses.
Hallucinations—where an LLM generates factually incorrect but confident-sounding text—are the biggest barrier to AI trust. Evaluating them systematically is the first step toward mitigation.
Detection Techniques
Use "N-shot" verification. Ask the model the same question multiple times with different temperatures. If the answers vary significantly in factual detail, it is a high-probability hallucination. You can also use "Self-Correction" prompts, asking the model to "critique your previous answer for factual accuracy," which often triggers the model to identify its own errors.
Automated Hallucination Scoring
Implement tools like Giskard or RAGAS that calculate a "Faithfulness" score. This involves comparing the model's output against a verified "ground truth" or the provided context in a RAG system. By quantifying hallucinations, you can set a "safety threshold" for your production applications.