How I Evaluate RAG Pipelines with RAGAS

The evaluation harness behind my production RAG work - faithfulness, answer relevance, and context precision as release gates.

RAGEvaluationRAGASLLM

Why evals are the whole job

DRAFT STUB — replace each section below with the real methodology, numbers, and traces from the production RAG work.

Most RAG demos die in production because nobody defined what "good" means before shipping. This post walks through the evaluation harness I used to take a RAG service from prototype to release-gated production system.

The three metrics that mattered

Faithfulness — is the answer grounded in the retrieved context, or is the model improvising?
Answer relevance — does the response actually address the query?
Context precision — how much of what we retrieved was worth retrieving?

Wiring RAGAS into CI

# TODO: real snippet from the eval pipeline
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision

results = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_precision])

Human review as ground truth

TODO: describe the Label Studio review loop and how LLM-as-judge scores were calibrated against human labels.

What the release gate caught

TODO: real regression examples — the chunking change that tanked faithfulness, and the router change that improved cost but degraded relevance.