· Generative AI · 1 min read
Evaluating Hallucinations: How to Trust Your RAG System
Your AI sounds confident, but is it lying? We explain how to build a 'RAG Triad' evaluation system using TruLens and Ragas.
The biggest fear with Generative AI is “Hallucination.” The model confidently inventing facts. In a chat application, it’s funny. In legal or medical advice, it’s a lawsuit.
The RAG Triad
To measure trust, we evaluate three links in the chain:
- Context Relevance: Did the Vector DB find useful documents? (Query -> Context)
- Groundedness: Is the answer supported by those documents? or did the AI make it up? (Context -> Answer)
- Answer Relevance: Did the AI actually answer the user’s question? (Answer -> Query)
Automated Eval (LLM-as-a-Judge)
We use tools like TruLens or Ragas. These tools use GPT-4 to read the User Query, the Retrieved Context, and the AI Answer, and score them 0-1.
- “The Context contains the refund policy, but the Answer talks about shipping. Groundedness = 0.2.”
The Feedback Loop
We run this evaluation on every single query in production. If the Groundedness score drops below 0.8, we flag the conversation for human review. This gives us a quantitative metric for “Truth.”
Can you trust your AI? Implement automated evaluation pipelines today. Get started.
