Lion Cipher Lab
Evaluate the answer quality of your RAG app
- AI Security
- Intermediate
- ~60 min
- Free · no account required
Why it matters
Building a RAG app is the easy part; knowing whether its answers are trustworthy is the part that actually decides if you can use it. Retrieval-Augmented Generation (RAG) means the app first RETRIEVES the most relevant passages from your own documents and pastes them into the prompt, so the model answers grounded in your text instead of only its training data. But grounding is not a guarantee: the retriever can miss the right passage, the model can still invent claims that go beyond what was retrieved (a 'hallucination'), and your documents themselves can be out of date. Evaluation is how you…
What you'll learn
- reasoning about how a RAG app fails (bad retrieval, hallucination, stale docs)
- the core RAG metrics in plain terms: retrieval recall, faithfulness, answer relevance
- building a small gold question-and-answer set over your own documents
- scoring answers by hand against a rubric
- running an open evaluation framework (Ragas) and reading its scores critically
- knowing the limits of LLM-as-judge and small test sets
Steps in this lab
- Why a working RAG app can still be wrong
- Split the pipeline: retrieval versus generation
- The core metrics, in plain terms
- Build a tiny gold set over your own documents
- Score answers by hand first
- Add an open framework: the Ragas concept
- Know the limits before you trust the number
- Why measuring is what makes RAG trustworthy