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🧪 RAG Evaluation Framework: A Practical Guide

As AI becomes embedded into every aspect of modern applications, evaluating AI-generated outputs is no longer optional—it's essential. In this post, we'll walk through how to set up a Retrieval-Augmented Generation (RAG) evaluation framework to detect hallucinations, monitor drift, and ensure factual consistency in your AI applications.

🚨 Designing an evaluation pipeline early avoids surprises in production when incorrect or hallucinated outputs can impact real users.

🎯 Why Build a RAG Evaluation Framework?​

We're in a transformative era where AI is a first-class citizen in software architectures. RAG systems combine retrieval with generation—making evaluations more nuanced than simple text classification.

Establishing a robust evaluation framework helps:

  • Measure performance using objective metrics
  • Track factual correctness and hallucination rate
  • Catch model drift over time
  • Provide actionable insights for model tuning

🔍 Core Components of a RAG Evaluation System​

To evaluate a RAG-based AI application, you typically need:

  • Evaluator Model (LLM-as-a-Judge)
  • Generator Model Output (LLM Response)
  • Reference Ground Truth (Expected Answer)
  • Context used by the model (retrieved documents)

📊 Evaluation Flow Overview

RAG Evaluation Flow Diagram

🧰 Using RAGAS for Evaluation​

We’ll use the open-source RAGAS framework to demonstrate how to calculate meaningful RAG evaluation metrics.

✅ Key Metrics​

Here are the metrics we’ll focus on:

  • Context Precision: % of relevant chunks in retrieved context
  • Context Recall: % of important chunks that were successfully retrieved
  • Context Entity Recall: % of key entities recalled in the context
  • Noise Sensitivity: Likelihood of incorrect responses from noisy input
  • Response Relevance: How well the answer aligns with the user input
  • Faithfulness: Is the response factually supported by the retrieved context?

⚙️ Setup Instructions​

Install required packages:

pip install langchain-aws ragas

📘 Sample Dataset for Evaluation​

We’ll use a simple geography Q&A dataset to show how RAGAS works:

GEOGRAPHY_QUESTIONS = [
    "Where is the Eiffel Tower located?",
    "What is the capital of Japan and what language do they speak?",
    ...
]

Each sample includes:

  • user_input
  • response from the AI
  • reference ground truth
  • retrieved_contexts used by the model
# Example Entry
user_input = "Where is the Eiffel Tower located?"
response = "The Eiffel Tower is located in Paris, France."
reference = "The Eiffel Tower is located in Paris, the capital city of France."
retrieved_contexts = ["Paris is the capital of France. The Eiffel Tower is one of the most famous landmarks in Paris."]

📊 Evaluation Results​

Once metrics are calculated using RAGAS, you’ll get a table like this:

QuestionContext RecallFaithfulnessFactual Correctness
Where is the Eiffel Tower located?1.01.01.0
What is the capital of Japan...?0.01.01.0
Which country has Rome...?0.6671.01.0

💾 Results saved to geography_evaluation_results.csv

🧠 Optional: Other Evaluation Options​

If you're already using AWS:

Metrics Avaiable​

The AWS Evaluation Service offers the following set of evaluation metrics.

Bedrock Metrics From Evaluation

Screenshots from AWS Evalaution Job​

Bedrock Evaluation 1

Bedrock Evaluation 2

Bedrock Evaluation 3

Sample Output from AWS Evaluation Job​

The Evaluation Job offers a user-friendly interface that presents various evaluation metrics in a clear, visual format—making it easy to review individual questions alongside their corresponding scores.

Bedrock Evaluation 4

🔗 Resources​

🚀 Wrapping Up​

This guide offers a foundational setup to get started with evaluating RAG pipelines. Whether you're validating hallucinations, tuning retrieval logic, or comparing generator outputs—RAGAS makes it easy to measure what matters.

Happy building & stay responsible with your AI systems! 🛠️