How to Evaluate LLM Hallucinations: A Practical Guide

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.