> ## Documentation Index
> Fetch the complete documentation index at: https://gtm-resouces.getmaxim.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How can I Detect Hallucinations in My AI Applications?

> RAG (Retrieval-Augmented Generation) evaluation measures the quality of both the retrieved context and the generated output in a RAG system. It uses metrics like answer correctness, relevance, semantic similarity, context recall, and faithfulness to assess how well the system retrieves and uses information to generate responses.

### Maxim AI’s Hallucination Detection Capabilities

Maxim AI provides comprehensive tools to detect and prevent hallucinations:

* **Automated Detection**: Run hallucination detection checks automatically on production traffic or test datasets.

* **Context Grounding Analysis**: Verify that responses are properly grounded in provided context and retrieved documents.

* **Multi-Method Detection**: Combine context comparison, external validation, and LLM-based evaluation for comprehensive coverage.

* **Alert Configuration**: Set up alerts when hallucination rates exceed acceptable thresholds.

* **Root Cause Analysis**: Identify patterns in when and why hallucinations occur to target prevention efforts.

By implementing robust hallucination detection, you can catch and correct false information before it reaches users, maintaining the reliability and trustworthiness of your AI application.