Retrieval-Augmented Generation (RAG) is a powerful technique for building applications that can answer questions using a private knowledge base. Instead of relying solely on its pre-trained knowledge, a large language model (LLM) is given access to a specific set of documents to inform its answers. RAG evaluation is the process of systematically measuring how well these systems perform, ensuring they are accurate, reliable, and helpful.

Evaluating a RAG system goes beyond checking for factual correctness. It involves a specific set of metrics designed to test the unique two-step process of RAG: retrieving relevant context and then generating an answer based on it. Without proper evaluation, you risk building an application that provides irrelevant answers, makes things up (hallucinates), or fails to use the information you’ve provided.

A RAG system has two core components: the Retriever and the Generator. The retriever’s job is to find the most relevant pieces of information from your knowledge base based on a user’s query. The generator’s job is to use that information—and only that information—to synthesize a coherent answer.

A robust evaluation framework tests both components, often using a “ground truth” dataset containing ideal question-and-answer pairs. The key metrics focus on the quality of the retrieved context and the final generated answer.

Here are the three most important metrics for any RAG system:

• Faithfulness: This metric asks, “Is the answer grounded in the provided context?” An answer is considered faithful if it is supported entirely by the information retrieved from the knowledge base. It’s a direct measure of hallucination. A low faithfulness score means your model is inventing information.
• Answer Relevancy: This assesses how well the generated answer addresses the user’s actual question. An answer can be factually correct and faithful to the source but completely miss the point of the query. High relevancy means the answer is not only accurate but also useful and on-topic.
• Context Recall: This measures the retriever’s performance. It asks, “Did the retriever find all the necessary information to answer the question thoroughly?” If the retrieved context is incomplete, even a perfect generator won’t be able to provide a complete answer.

This table summarizes what each metric measures and why it’s important.

These metrics work together to give you a holistic view of your system’s performance, helping you pinpoint whether a bad response is the fault of the retriever or the generator.

Careful evaluation is the difference between a demo-worthy prototype and a production-ready, reliable application. It’s a critical practice for building user trust and ensuring your AI application behaves as expected.

Here’s why it’s a non-negotiable part of the development lifecycle:

• Builds user trust: When users get answers that are consistently accurate and grounded in a trusted knowledge source, they learn to rely on your application. Hallucinations and irrelevant answers erode that trust quickly.
• Prevents misinformation: RAG systems are often used for specialized knowledge domains, like internal company documentation or technical manuals. In these contexts, a hallucinated answer isn’t just wrong—it can be misleading or even dangerous.
• Enables systematic improvement: You can’t fix what you can’t measure. By tracking metrics like faithfulness and context recall, you can identify specific weaknesses. For example, a low context recall score tells you to focus on improving your retrieval strategy, while a low faithfulness score points to issues with your generation prompt or model.
• Provides a safety net for regressions: As you experiment with new retrieval techniques, chunking strategies, or LLMs, a solid evaluation suite ensures you don’t accidentally make the system worse.

Getting started with RAG evaluation doesn’t have to be overly complex. A practical, iterative approach is often the most effective way to build confidence in your system.

You don’t need thousands of data points to get meaningful insights. Begin by manually creating a small, “golden” dataset of 50-100 examples. Each example should include:

• A representative user question.
• The ideal, hand-written answer.
• The specific chunks of context from your knowledge base required to write that answer.

This dataset becomes your ground truth, allowing you to compare your system’s output against a known-good standard.

No single metric tells the whole story. A system might have high answer relevancy but low faithfulness, indicating that the LLM is answering the question correctly but ignoring the provided context. Looking at faithfulness, relevancy, and recall together gives you a balanced view of performance and helps you diagnose problems more effectively.

Your evaluation dashboard will tell you what is wrong, but not why. Set aside time to manually review the failures. For each incorrect answer, ask:

• Was it a retrieval problem? Did the retriever fail to find the right documents?
• Was it a generation problem? Was the context correct, but the LLM failed to synthesize a good answer from it?
• Is the “ground truth” wrong? Sometimes, your evaluation data has errors.

Categorizing these failures will help you prioritize your efforts, whether that means refining your embedding model, tweaking your generation prompt, or cleaning your source documents.

Data leakage occurs when the LLM already knows the answer to a question from its pre-training data, bypassing the context you provide. This can artificially inflate your evaluation scores, as the model appears to be faithful and relevant without actually using your documents. You can spot this by checking if the generated answer contains information that isn’t present in the retrieved context.

While RAG evaluation focuses on the quality of AI-generated content, it’s equally important to secure the application that delivers it. RAG systems are often used to provide access to private or sensitive information, making robust security a top priority.

Kinde provides the authentication and authorization layer needed to ensure that only the right users can access your RAG application and its underlying data.

• Secure your endpoints: Your RAG application likely communicates through APIs. Kinde helps you protect these APIs, ensuring that every request is authenticated and authorized, which is crucial for controlling access and monitoring usage.
• Protect against attacks: As with any application, AI-powered tools can be targets for abuse. Kinde’s built-in features like brute-force and credential enumeration protection help secure your application against common threats.
• Manage user data responsibly: If your RAG system handles any user data, complying with regulations like GDPR is essential. Kinde provides a foundation for building compliant applications, helping you manage user consent and data privacy.

By handling the security infrastructure, Kinde lets you focus on what makes your application unique: the quality and performance of your RAG system.

• API best practice for AI apps
• Configure attack protection
• General data protection regulation (GDPR)