Evaluating LLM Agents: A Technical Guide to RAGAs and G-Eval Frameworks

A Hands-On Guide to Testing Agents with RAGAs and G-Eval

RAGAs and DeepEval provide a systematic, LLM-driven approach to replace subjective ‘vibe checks’ in agent evaluation. These frameworks quantify essential properties like contextual accuracy and answer relevance using metrics such as faithfulness.

Why This Matters

Modern AI development often relies on subjective manual review, which fails to scale as agent complexity increases with reasoning and tool execution. By implementing automated benchmarks like faithfulness and coherence (thresholded between 0 and 1), engineers can move from anecdotal testing to a rigorous, data-driven validation pipeline that ensures production reliability.

Key Insights

• RAGAs assesses the ‘RAG triad’ of properties, including faithfulness, which measures how well generated answers align with provided context.
• DeepEval leverages G-Eval to assess qualitative attributes such as coherence and professionalism through natural language criteria and reasoning-based scoring.
• Hugging Face Dataset objects are the standard structure for representing test cases in RAGAs pipelines to ensure efficient data handling.
• Effective agent evaluation requires a combination of structured metrics for quantitative accuracy and reasoning-and-scoring layers for qualitative assessment.

Working Examples

Evaluating a RAG-based test case using the RAGAs framework for faithfulness and answer relevancy.

import os
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from datasets import Dataset

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
test_cases = [
    {
        "question": "How do I reset my password?",
        "answer": "Go to settings and click 'forgot password'. An email will be sent.",
        "contexts": ["Users can reset passwords via the Settings > Security menu."],
        "ground_truth": "Navigate to Settings, then Security, and select Forgot Password."
    }
]
dataset = Dataset.from_list(test_cases)
ragas_results = evaluate(dataset, metrics=[faithfulness, answer_relevancy])
print(f"RAGAs Faithfulness Score: {ragas_results['faithfulness']}")

Using DeepEval’s G-Eval implementation to measure qualitative coherence with LLM-driven reasoning.

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams

coherence_metric = GEval(
    name="Coherence",
    criteria="Determine if the answer is easy to follow and logically structured.",
    evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
    threshold=0.7
)

case = LLMTestCase(
    input="How do I reset my password?",
    actual_output="Go to settings and click 'forgot password'. An email will be sent."
)

coherence_metric.measure(case)
print(f"G-Eval Score: {coherence_metric.score}")
print(f"Reasoning: {coherence_metric.reason}")

Practical Applications

• Use Case: Technical support systems using RAGAs to ensure generated password reset instructions are faithful to the ‘Settings > Security’ documentation.
• Pitfall: Relying solely on ‘vibe checks’ for agent behavior, which leads to inconsistent outputs and unquantifiable regression risks during model updates.

References:

• https://machinelearningmastery.com/a-hands-on-guide-to-testing-agents-with-ragas-and-g-eval/