RAG Eval Sets: The Secret to a Reliable AI Product

Imagine an AI system advising a doctor on treatment protocols or summarizing a patient’s medical history. A single hallucinated fact could be disastrous. This is why Retrieval-Augmented Generation (RAG) is essential: it anchors the LLM to a verified knowledge source (like internal clinical guidelines or legal documents), ensuring the answer is accurate, up-to-date, and fully auditable. The RAG pipeline turns an unreliable, general-purpose LLM into a dependable, specialized product. However, based on my experience, we need to engineer an effective Evaluation (EVAL) set to ensure that our Rag-based AI system is reliable.

The Foundation: Three Factors that Determine Difficulty

1. Context Relevance: Does the system retrieve documents that actually relate to the user’s question?

2. Context Sufficiency: Does the system retrieve all necessary facts to fully answer the question, or is the evidence scattered across multiple documents? This tests the system’s ability to handle fragmented knowledge.

3. Format Constraints: Can the system stick to the format we asked for? Does it deliver the answer as a perfect JSON object, a numbered list, or with the correct citations included?


Engineering the Eval Set: How to Build a Test Lab

Creating an enterprise-grade Eval Set is like designing a controlled science experiment for your product. It requires three core steps:

1. The Golden Triple (Q, A, Ground Truth Context)
Every test case must start with the Golden Triple: a Question (Q), the perfect Answer (A), and the Ground Truth Context. Having the Ground Truth Context lets you separate the errors, which is critical for product development. Given the retrieved documents, was the generated answer supported only by those facts? If not, the LLM is likely hallucinating.

2. Stress Testing and Robustness
While the Golden Triple ensures accuracy on expected queries, building truly resilient systems requires stress testing – a topic so critical it deserves its own deep dive. We will cover it in another post to avoid distraction.

3. The Continuous Loop (Versioning)
The validity of your Eval Set degrades as your company’s knowledge base changes. If you add a new policy or retire an old one, your tests might become meaningless.

In conclusion, an effective RAG Eval Set is a strategic tool. It moves beyond simple pass/fail tests and provides the quantifiable data you need to ensure your AI product is not just smart, but reliably safe, accurate, and ready for the enterprise.

Note: HumanLens.ai brings proven experience in quality and risk management to build the necessary technical and procedural guardrails around your RAG pipeline, ensuring your AI product is not just accurate, but auditable and safe.