As businesses increasingly lean on retrieval-augmented generation (RAG) to provide accurate, real-time answers to customers and teams, measuring how well these systems perform becomes critical. With new models capable of managing huge amounts of context, it’s tempting to assume accuracy will automatically improve. But in practice, bigger isn’t always better, and RAG systems, especially complex ones, need careful and continuous evaluation.
This article will walk you through practical steps to evaluate your RAG system from a business perspective. You’ll learn straightforward methods to test accuracy, reliability, and real-world usefulness. We’ll also take a close look at how SuperAnnotate helped Databricks refine their RAG approach—ultimately cutting costs and boosting performance—so you can walk away with clear insights for improving your own setup.
Retrieval-augmented generation (RAG) components
When you’re setting up a RAG system, it’s tempting to assume that picking the top-ranked models from HuggingFace will give you the desired results. After all, they’ve already proven themselves on various benchmarks. But real-world data isn’t always a perfect match for test sets, and simply chasing benchmark scores can overlook important nuances in your specific use case. That’s why it helps to understand each of your system’s core pieces:
- An embedding model that encodes document passages as vectors.
- A retriever that takes a question, converts it into the same vector space, and returns nearby documents.
- A reranker (optional) that refines relevance scores when given a question and a specific document.
- A language model that receives the documents from the retriever or reranker, alongside the question, and then generates an answer.
In practice, there are more reliable ways to evaluate a RAG system than just looking at a model’s performance on paper. By focusing on how these components work together with your own data—and what matters most to your business—you can catch potential pitfalls early and build a more reliable setup.
The problem with standard benchmarks
Public benchmark ratings often come from datasets that look nothing like your internal or domain-specific data, which may cause some problems:
- Queries that appear nearly identical to an outsider can hold meaningful distinctions for a subject-matter expert. General-purpose embedding models may miss these subtle differences.
- If your reranker isn’t trained to handle specialized language or acronyms, it may incorrectly judge relevant results as irrelevant, and vice versa.
- Formatting and instructions: Large language models can stumble over abbreviations, custom request formats, or instructions that fall outside the scope of their typical training examples.
RAG evaluation assessment
Now that we've discussed potential issues in a RAG pipeline, the next step is determining how to measure your system's effectiveness clearly and practically. It's important to look beyond simply checking if the final answers seem correct and instead focus on identifying which specific areas—like document retrieval or answer generation—may need improvement. Defining clear evaluation criteria will help you easily compare different system versions or configurations and understand precisely which adjustments lead to better performance.
Key areas to assess
1) Document relevance
Check if the documents retrieved consistently address the user’s actual questions. If the documents aren’t relevant, even the best language models will struggle to produce accurate answers.
2) Reranking improvements (if used)
If you include a reranker to prioritize the best documents, check whether it genuinely improves relevance. Sometimes adding extra complexity to the pipeline doesn’t yield the expected performance jump, so it’s important to confirm that the reranking step does more good than harm.
3) Answer accuracy
Even with the correct documents available, the language model still needs to generate accurate answers. Ask whether your final outputs accurately reflect the facts in those documents. A good way to test this is by comparing responses to an established “ground truth” or having a knowledgeable reviewer verify them.
4) Hallucination checks
Language models can sometimes introduce plausible but incorrect details that aren't present in the retrieved documents. Monitoring LLM hallucinations closely is essential, especially in high-stakes contexts like healthcare, finance, or legal fields.
Creating a uniform grading rubric
To ensure consistent evaluation, it's beneficial to create a clear and standardized grading rubric. Having a shared set of criteria—such as specific guidelines for correctness or instructions for identifying hallucinations—helps maintain uniformity, whether your team evaluates manually or uses automated methods. A structured approach minimizes confusion, simplifies comparisons, and clarifies precisely where your RAG system can be improved.
RAG component pitfalls
With your evaluation framework ready, you can easily identify where improvements are needed. Start by testing a variety of questions and scoring the results using your grading rubric. Low scores indicate the components requiring the most immediate attention. Here’s a clear overview of improving each RAG component:
Embedding model
If your evaluation shows low scores in document relevance, your embedding model may struggle to identify documents correctly. Embedding models convert text into numerical vectors, storing these vectors in databases. When questions are asked, they’re transformed into vectors and matched with similar document vectors.
Reranker
Rerankers reorder the initial results provided by embeddings, aiming for better relevance. Unlike embeddings that compress text into vectors (which can lose subtle nuances), rerankers operate directly on the original text, performing a more detailed comparison.
Language model (LLM)
The final step, where the LLM generates answers, may also require adjustments if the answers it provides are frequently incorrect or contain hallucinations. Start by evaluating multiple language models to identify the one that fits your requirements best. For instance, large models like GPT-4 usually deliver strong results, but might not be suitable for all scenarios due to cost or data privacy concerns.
RAG evaluation with SuperAnnotate
Building a RAG system often comes with unexpected hurdles, from deciding which models best fit your data to ւնդեռստանդինգ why certain outputs aren’t quite right. SuperAnnotate simplifies these challenges, making it easier for your team to build and refine an effective RAG pipeline without the headaches.
Here's how SuperAnnotate can help you get the most from your RAG setup:
- Finding the right models: Instead of guessing or relying on general benchmarks, SuperAnnotate helps you test and identify embedding and language models that truly fit your specific domain and business needs.
- Finding hidden issues: Our evaluation process clearly highlights the parts of your system that aren't performing well, so you can quickly prioritize your improvement efforts.
- Better embeddings through better data: Easily create robust, domain-specific datasets that improve embedding accuracy and ensure your retriever consistently finds relevant content.
- Collaborative and easy-to-use platform: SuperAnnotate’s intuitive interface makes it simple for everyone—even team members without technical backgrounds—to contribute meaningfully. This collaboration speeds up the dataset creation process and ensures accuracy across the board.
Continuous improvements made simple
SuperAnnotate's annotation tools let you clearly see every step of your RAG evaluation—from the original query to the documents retrieved and the final output. With this clarity, reviewers can quickly catch problems, refine annotations, and produce datasets that consistently enhance system performance. By using SuperAnnotate, your team can confidently scale a RAG system that reliably delivers accurate, useful answers tailored to your real-world business goals.
Closing notes
Evaluating and improving your RAG system doesn't have to be overwhelming. By setting clear criteria, using targeted evaluation methods, and leaning on reliable tools like SuperAnnotate, you can confidently navigate the complexity and consistently deliver accurate, helpful responses. Continuous evaluation ensures your system evolves with your business, staying sharp, relevant, and effective for your teams and customers.
