Evaluating AI Safety: How to Build Hallucination Audits for LLMs
Setting up automated validation frameworks to test model facts and safeguard corporate deployments.
#AI Safety
#Auditing
#Hallucinations
How do you guarantee your customer support bot does not offer a free refund? Building safety checks is critical for public-facing AI deployments.
Automated Hallucination Auditing
We utilize LLM-as-a-judge frameworks (using packages like TruLens or Giskard) to audit responses. These systems automatically test generation against three main metrics:
- Context Adherence: Is the response derived solely from the provided RAG documents?
- Answer Relevance: Does the response address the user’s actual question?
- Grounding: Are there contradictions between the generated text and factual baselines?