Automated RAG Quality Evaluation with RAGAS

We are a team of AI engineers with over 7 years of experience in NLP and 30+ RAG systems delivered. Guaranteed metrics improvement or a detailed report. Without systematic RAGAS evaluation, the RAG system remains a black box: you don't know how many hallucinations the model generates or how much relevant context is lost. RAGAS (RAG Assessment) is the most popular framework for automated evaluation, using an LLM as a judge and requiring no manual labeling. On one project, we reduced manual answer verification by 80%, saving the team over $30,000 per year in evaluation costs and significantly cutting the evaluation budget. In 3–6 weeks, we set up the full evaluation pipeline, integrate it into CI/CD, and drive metrics to 0.9+. Contact us for an audit of your RAG system.

What is RAGAS and why do you need it?

RAGAS is an open-source framework for evaluating the quality of RAG systems. It automatically generates test sets from your documents and evaluates LLM answers across five key metrics. Without RAGAS, you evaluate quality manually or not at all, leading to uncontrolled hallucinations and context loss. RAGAS makes evaluation objective and reproducible. Learn more on GitHub.

RAGAS Metrics

How to interpret RAGAS metrics?

Context Precision below 0.7 signals that the system retrieves a lot of irrelevant context. Solutions: improve reranking, add metadata filtering, reduce top_k.

Context Recall below 0.7: the system fails to find the needed documents. Solutions: improve chunking, try hybrid search, fine-tune embedding models.

Faithfulness below 0.8 is a critical signal: the model hallucinates, making up information not supported by the context. Solutions: improve system prompt, add an instruction to answer only based on context, lower temperature. This is the most important metric for legal and medical RAG systems.

Answer Relevancy below 0.8: answers are off-topic. Solutions: improve prompt, add few-shot examples of the desired format.

How to implement RAGAS in 4 steps

1. Prepare your documents and create a test set (at least 200 questions). RAGAS will automatically generate questions of varying difficulty.
2. Run a baseline evaluation on all metrics. This takes 1–2 days.
3. Analyze the results and perform optimization iterations (improve retrieval, prompt, chunking).
4. Integrate continuous evaluation into CI/CD — now every commit will be checked for quality degradation.

Setting up the pipeline takes 2–3 days, test generation 1–2 days, and the full cycle with improvements 3 to 6 weeks.

Installation and basic usage of RAGAS

from ragas import evaluate
from ragas.metrics import (
    context_precision,
    context_recall,
    faithfulness,
    answer_relevancy,
    answer_correctness,
)
from datasets import Dataset

# Prepare dataset for evaluation
eval_data = {
    "question": [
        "What is the contract duration?",
        "Who is responsible for delivery delay?",
    ],
    "answer": [
        "The contract is valid until December 31.",
        "The supplier is responsible for delays exceeding 5 business days.",
    ],
    "contexts": [
        ["2.1. This Agreement comes into force upon signing and is valid until December 31..."],
        ["4.3. In case of delivery delay beyond 5 business days, the Supplier..."],
    ],
    "ground_truths": [
        "The contract is valid until December 31.",
        "The Supplier is responsible for delays exceeding 5 business days.",
    ],
}

dataset = Dataset.from_dict(eval_data)

# Evaluation
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper

evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
evaluator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())

results = evaluate(
    dataset,
    metrics=[context_precision, context_recall, faithfulness, answer_relevancy],
    llm=evaluator_llm,
    embeddings=evaluator_embeddings,
)

print(results)
# {'context_precision': 0.88, 'context_recall': 0.82, 'faithfulness': 0.94, 'answer_relevancy': 0.91}

Automated test set: Testset Generation

RAGAS can generate a test set from your documents:

from ragas.testset.generator import TestsetGenerator
from ragas.testset.evolutions import simple, reasoning, multi_context

generator = TestsetGenerator.with_openai()

# Generate tests of varying difficulty
testset = generator.generate_with_langchain_docs(
    documents=your_documents,
    test_size=100,
    distributions={
        simple: 0.5,          # Simple questions from one document
        reasoning: 0.3,       # Requires reasoning
        multi_context: 0.2,   # Requires multiple documents
    }
)

testset.to_pandas().to_csv("evaluation_testset.csv", index=False)

Practical case: iterations on a legal chatbot RAG system

From our practice: a client — a legal firm with a corpus of 10,000 contracts. The initial RAG on GPT-4o-mini and ChromaDB showed low metrics. We performed three optimization iterations.

Initial state (v1):

Iteration 1: added hybrid search (sparse + dense). Context Recall increased from 0.68 to 0.81 (+19%).

Iteration 2: added Contextual Compression and reranker. Context Precision improved from 0.61 to 0.84 (+38%), Faithfulness from 0.74 to 0.91 (+23%).

Iteration 3: refined system prompt with explicit anti-hallucination instructions. Faithfulness reached 0.95, Answer Relevancy 0.88.

Final state (v4):

RAGAS allowed us to automate evaluation, making it 10x faster than manual reviewer assessment. The budget savings on evaluation amounted to tens of thousands of dollars on each project.

Continuous Evaluation in CI/CD

import pytest

@pytest.fixture(scope="session")
def rag_evaluation_results():
    """Run RAGAS evaluation on the test set"""
    return evaluate(evaluation_dataset, metrics=[faithfulness, context_recall])

def test_faithfulness_above_threshold(rag_evaluation_results):
    assert rag_evaluation_results["faithfulness"] >= 0.85, \
        f"Faithfulness {rag_evaluation_results['faithfulness']:.2f} below threshold 0.85"

def test_context_recall_above_threshold(rag_evaluation_results):
    assert rag_evaluation_results["context_recall"] >= 0.75

What's included in the service

• Setting up the RAGAS pipeline tailored to your infrastructure.
• Generating a representative test set (200+ questions).
• Baseline evaluation on 5 metrics.
• 2–3 optimization iterations with specific recommendations.
• Integrating continuous evaluation into your CI/CD.
• Access to an evaluation dashboard and detailed documentation.
• A training session for your team (up to 2 hours).
• 3 months of email support for any follow-up questions.
• A report with results and a plan for further optimization.

Trusted by leading legal and financial institutions. Over 5 years of experience in RAG systems. We guarantee to improve your RAG metrics or provide a comprehensive diagnostic report at no extra cost.

Get a consultation on your RAG system evaluation — we will assess your project and propose a plan. Order a metrics audit now.