Ragas

Reference-Free Evaluation for RAG Pipelines & LLM Applications

What is Ragas?

Ragas (Retrieval-Augmented Generation Assessment) is an open-source framework focused on evaluating RAG pipelines. Its defining feature is reference-free evaluation: many of its metrics use an LLM to score quality without needing hand-labeled ground-truth answers, dramatically lowering the cost of building eval datasets. It plugs into LangChain, LlamaIndex and observability tools like Langfuse.

"Ragas decomposes a RAG answer into atomic claims and checks each one against the retrieved context — turning the fuzzy question 'is this answer good?' into measurable scores for faithfulness, relevancy, and retrieval quality."

— Ragas documentation

The Core Metrics

Ragas separates retrieval quality from generation quality, so you can pinpoint where a pipeline fails.

Metric	Stage	What It Measures
Faithfulness	Generation	Share of answer claims grounded in the retrieved context (anti-hallucination)
Response / Answer Relevancy	Generation	How directly the answer addresses the question (no padding/off-topic)
Context Precision	Retrieval	Are the relevant chunks ranked at the top? (signal vs. noise)
Context Recall	Retrieval	Did retrieval fetch all the info needed to answer?
Noise Sensitivity	Retrieval	How easily irrelevant chunks corrupt the answer
Factual Correctness	End-to-end	Agreement with a reference answer (when one exists)

Quick Start

pip install ragas

from ragas import evaluate
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import Faithfulness, ResponseRelevancy, LLMContextPrecisionWithoutReference

sample = SingleTurnSample(
    user_input="What is the capital of France?",
    response="The capital of France is Paris.",
    retrieved_contexts=["France is in Europe. Its capital is Paris."],
)

result = evaluate(
    dataset=[sample],
    metrics=[Faithfulness(), ResponseRelevancy(), LLMContextPrecisionWithoutReference()],
)
print(result)  # {'faithfulness': 1.0, 'answer_relevancy': 0.98, ...}

When to Reach for Ragas

Great Fit

Diagnosing RAG: is it retrieval or generation that's broken?
Evaluating without labeled ground-truth answers
Comparing chunking, embedding or retriever configs
Generating synthetic test sets from your documents

Keep in Mind

Metrics rely on a judge LLM — quality and cost depend on it
Scores can vary run-to-run; average over a dataset
Focused on RAG; not a general agent test runner
Calibrate thresholds against human judgment

Resources

GitHub

Source, examples and the full metric catalogue.

github.com/explodinggradients/ragas →

Documentation

Metric reference and integration guides.

docs.ragas.io →