Qlarify Labs
Evals
How we evaluate AI systems to find their limits. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
3 methods
Oracle
Groundedness & attribution checking
Decompose an answer into its claims and check each one against the retrieved context — is the answer actually supported by what was retrieved, or did the model fill in the gaps?
EmergingHallucinationEvalsRAG
BoundaryNeedle-in-a-haystack (long-context retrieval)
Plant a specific fact at varying depths in a long context and test whether the model can retrieve it from each position.
EstablishedRAGContext window
OtherRetrieval evaluation (RAG)
Measure the retriever on its own terms — did the right documents come back, and how far up the ranking — before judging anything the model wrote with them.
EstablishedEvalsRAG