RAG Triad Evaluation (Ragas)

RAG Triad Evaluation (established by TruLens and Ragas frameworks) is the industry-standard modular evaluation framework that decomposes RAG into Query $Q$, Retrieved Context $C$, and Generated Answer $A$, evaluating 3 orthogonal unsupervised quality metrics: 1) Context Relevance (evaluating the density of useful evidence in $C$ relative to $Q$, grading retrieval precision); 2) Groundedness / Faithfulness (verifying every atomic claim in $A$ is strictly supported by $C$, grading hallucination absence); 3) Answer Relevance (evaluating if $A$ directly answers $Q$, grading responsiveness).

Production RAG CI/CD pre-flight gatekeeping, modular component failure isolation, and automated regression benchmarking.

Black-box end-to-end evaluation cannot isolate whether a poor final answer stems from retrieval failure or generation hallucination; the RAG Triad decouples retrieval performance from LLM reasoning for pinpoint root-cause diagnosis.

Derive the mathematical formula and prompt engineering workflow for Context Relevance in Ragas?

How does Faithfulness compute $S = \frac{|V|}{|T|}$ via claim extraction and NLI entailment against context passages?

How does Answer Relevance score ungrounded outputs by generating synthetic reverse questions $q_i$ and embedding cosine similarity?

How to optimize LLM API cost and evaluation latency when running Ragas over massive 10K+ token retrieved contexts?

How to correlate automated RAG Triad scores with real user telemetry (thumbs up/down) in production dashboards?