Hallucination Detection

Hallucinations are one of the most critical risks in production AI systems. These guides help you understand, detect, and prevent fabricated outputs that could harm your users and business.

In This Section

Hallucination Types Classify different categories of AI hallucinations.

Hallucination Benchmarks Evaluate models using standardized hallucination metrics.

Prompt Accuracy Design prompts that minimize hallucination risk.

RAG Grounding Ground responses in retrieved context to reduce hallucinations.

Hallucination Filtering Implement runtime filters to catch fabricated content.

Fine-tuning for Factuality Train models to produce more accurate outputs.

Knowledge Cutoff Handle queries beyond the model's training data.

Semantic Drift Detect when models drift from factual accuracy over time.