What are AI hallucination evaluations? Metrics and methods that work in

Hallucination evaluation in AI systems is a comprehensive process that assesses whether outputs are factually incorrect, unsupported by retrieved sources, or inconsistent with previous context. Effective evaluation requires a combination of suitable metrics, scoring methods, and reference sources to align with potential failure modes in production, whether those involve retrieved documents, curated datasets, or repeated outputs without a reference. Metrics like groundedness, faithfulness, factuality, and consistency each address different aspects of hallucination, necessitating separate checks for each failure mode and highlighting the importance of choosing the right evaluation setup. Braintrust offers an integrated workflow that combines built-in scorers, custom scorers, and human review to transition hallucination checks from measurement to release control, supporting tools like LLM judges, fine-tuned models, and semantic entropy for varied evaluation needs. By utilizing human reviews for calibration and employing methods like consistency sampling and semantic entropy, evaluations can be tailored to specific tasks, ensuring that automated scoring aligns with real-world user risks and improving system reliability over time.