How to Test a RAG System: RAGAS, Hallucination Detection, and the Complete GenAI CI/CD Pipeline
Series: Enterprise GenAI & RAG Architecture — Part 5 of 5
Why Testing a RAG System is Different
Testing a traditional API is straightforward: given input X, expect output Y. Testing a RAG system is categorically different. You are simultaneously validating:
- Data quality — are the right documents ingested correctly?
- Chunking quality — are chunks meaningful and complete?
- Embedding quality — do similar topics score highly together?
- Retrieval accuracy — are the most relevant chunks returned?
- LLM output quality — is the answer grounded, accurate, and complete?
- Security — is the system resistant to prompt injection and PII leakage?
Each layer can fail independently. A perfect LLM with bad retrieval still produces bad answers. This is why RAG testing requires a layered, systematic approach.
The most expensive RAG failure is silent degradation — retrieval quality slowly drifts as documents change, and nobody notices until users stop trusting the system.
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