How to evaluate RAG systems: metrics, frameworks & infrastructure

Your Retrieval Augmented Generation (RAG) system works perfectly in demos, then production users report irrelevant answers and hallucinated facts. The retrieval finds documents, the LLM generates responses, but somewhere between chunking and generation, quality breaks down. It's clearly time to evaluate your RAG system, but how do you do that?

In this article, you'll learn how to measure RAG system performance across retrieval and generation stages, frameworks that automate evaluation at scale, and production practices that catch failures before users do.

What is RAG evaluation

RAG changed how we build AI systems by letting LLMs pull in external information instead of relying only on training data. You get accurate, current, and verifiable text generation, but only if retrieval and generation both work. RAG evaluation shows whether your system actually retrieves the right documents and generates accurate answers.

RAG has a modular design: chunking, retrieval, reranking, context assembly, and generation. This architecture makes RAG great at improving factuality by grounding LLM outputs in external sources, but it also introduces new failure modes across these stages. When answers go wrong, you'll need to know whether retrieval grabbed irrelevant documents, the LLM hallucinated despite good context, or ranking buried critical information at position 47. You need RAG evaluation to figure out exactly what's broken and why.

RAG evaluation comes down to three core dimensions:

• Context relevance (sometimes called context precision) measures whether your retrieval system finds documents that actually matter for the query.
• Groundedness (also called faithfulness) checks if generated responses stay true to retrieved context without inventing facts.
• Answer relevance evaluates whether the final output addresses what users actually asked.

Miss any of these dimensions and you're optimizing the wrong bottleneck.

Why RAG evaluation matters for production systems

Production breaks RAG systems in ways demos never reveal. Scale exposes cascading failures, costs spiral from redundant queries, and quality silently degrades while your manual testing falls further behind.

You can't optimize what you don't measure

RAG systems have multiple stages (chunking, retrieval, reranking, generation). If any stage fails, you'll have more failures cascading down the system.

For example, your retrieval might achieve 0.85 precision but bury critical documents at position 15 where the LLM never sees them. Or retrieval might work perfectly but generation will hallucinate because your prompt doesn't emphasize grounding.

Evaluation metrics for each stage tell you exactly where to focus optimization efforts instead of guessing which component broke.

Unmonitored costs scale faster than usage

Production deployments can spend significant money on LLM API calls, with a substantial portion of queries being similar to previous requests. Without evaluation metrics tracking cache hit rates and query similarity distributions, you won't know you're burning money on redundant calls.

Manual testing can't keep pace with production scale

Your RAG system handles thousands of queries daily across different user types, domains, and edge cases. Manual review catches maybe a few hundred queries per week. Automated evaluation will give you consistent scoring across your entire test suite in minutes.

These frameworks check for context relevance, faithfulness, and answer relevance, letting you catch regressions before deployment and track quality trends over time. Human review still validates edge cases and checks LLM-as-judge reliability, but automated metrics make continuous production monitoring actually work.

Quality degradation is an ever-present threat

Imagine a code change slightly shifting your embedding space, or a new data source introducing formatting inconsistencies. Without evaluation baselines and regression testing, these changes will silently degrade quality for weeks before users report problems. Systematic evaluation will catch failures early when they're cheap to fix.

Key metrics for RAG systems

When you're running RAG evaluation at production scale, you need metrics at both the retrieval stage and the evaluation stage. Let's break down the specific metrics involved in this.

Retrieval quality metrics

Retrieval quality metrics measure how well your system finds and ranks relevant documents before they reach the LLM.

• Precision@K and Recall@K measure whether your system finds relevant documents. Precision@K divides relevant documents in your top-K results by K total results. If you retrieve 10 chunks and 7 are relevant, that's 0.7 precision. Recall@K divides relevant documents retrieved by total relevant documents available.
• Mean Reciprocal Rank (MRR) measures ranking quality by checking how fast users find relevant results. The formula calculates the average of 1/rank for the first relevant document across all queries. If your first relevant document appears at position 2, you get 0.5. Position 1 gives you 1.0. An MRR above 0.8 indicates most queries return relevant results in the top 1-2 positions, though actual targets vary by domain and query complexity.
• Normalized Discounted Cumulative Gain (NDCG) evaluates ranking quality when documents have varying relevance levels rather than binary relevant/irrelevant labels. The formula applies logarithmic position discounts, heavily penalizing relevant documents ranked low.

You need to track retrieval metrics because retrieval failures cascade downstream. If your retriever misses critical documents or buries them at position 20, the LLM will never see them. You'll get hallucinations or incomplete answers, and generation metrics alone won't tell you why.

Generation quality metrics

Generation quality metrics evaluate what the LLM does with retrieved context.

• Faithfulness (also called groundedness) measures whether generated answers stay true to retrieved context without hallucinating. The calculation works like this: extract individual statements from the generated answer using an LLM, classify each as supported or unsupported by context, then divide supported statements by total statements. Production systems handling high-stakes apps (medical, legal, financial) need faithfulness >0.9. Standard production systems should maintain >0.8 to avoid hallucination problems. Note that LLM-based evaluation can introduce its own biases, so some teams add rule-based chunk comparison as a validation step.
• Answer relevancy checks if the generated response actually addresses what the user asked. The metric uses an inverse approach: generate multiple hypothetical questions from your answer using an LLM, calculate semantic similarity between those questions and the original query, then average similarity scores. This metric is important for user experience, because responses can be factually accurate yet fail to address the user's actual information need. Target >0.7-0.8 for acceptable user experience.
• Context precision and context recall bridge retrieval and generation by evaluating the quality of retrieved context. Context Precision measures whether relevant chunks appear early in your ranked list rather than scattered throughout results. Context Recall checks if all necessary information appears in retrieved chunks. When Context Recall drops below 0.8, it means your LLM doesn't have necessary information and might fabricate missing details.

It's important to measure generation quality matters because good retrieval doesn't guarantee good answers. Your retriever might surface perfect documents, but the LLM can still hallucinate, ignore key context, or miss the point of the question entirely. Generation metrics catch failures that retrieval metrics can't see.

How to automate RAG evaluation at scale

You can't manually review thousands of queries a day, and your system won't wait for you to catch up. Automated evaluation runs continuously, scoring retrieval quality and generation accuracy across your entire query volume while you're shipping features. Here's what you need to do to automate RAG evaluation:

Choose evaluation frameworks that scale

The most widely adopted frameworks calculate context precision, context recall, faithfulness, and answer relevancy using LLM-as-judge approaches. These frameworks integrate with major cloud platforms and can be used with evaluation frameworks for production evaluation pipelines. You get standardized metrics without building evaluation infrastructure from scratch. When ground truth is scarce, research-focused frameworks from institutions like Stanford automate assessment through synthetic data generation with statistical confidence intervals.

Keep in mind that LLM-as-judge reliability varies by model and prompt design. Run periodic human spot checks to validate that automated scores align with actual quality, especially after changing models or evaluation prompts.

Automate quality checks in your deployment pipeline

Integrate evaluation into your CI/CD pipeline using pytest or similar testing frameworks. Set metric thresholds that automatically fail builds when quality degrades. Define acceptable ranges for each RAG Triad dimension (context relevance, faithfulness, and answer relevance) and catch regressions before they reach production. This shifts evaluation left in your development process, preventing quality issues rather than discovering them after deployment.

Choose infrastructure that can handle production scale

Evaluation frameworks only give you metrics. To run evaluations at production scale, you need infrastructure that stores test data, processes results, and tracks quality over time. Redis provides the foundation these frameworks need: vector search for retrieval quality testing, semantic caching to validate cost savings, and Redis Streams for logging evaluation results. Redis handles the data layer by storing test cases, embeddings, and evaluation results, while frameworks handle the scoring logic.

Start evaluating your RAG system today

You need unified infrastructure to automate RAG evaluation at production scale. It's just too complex to manage separate tools for vectors, caching, and message streaming, and just increases failure modes in the system.

Redis consolidates these capabilities with consistent APIs and shared operational infrastructure, letting you measure end-to-end performance without vendor sprawl.

Redis handles the full evaluation workflow:

• Store test cases, embeddings, and evaluation results with configurable topK parameters for retrieval testing
• A/B test cache effectiveness by adjusting similarity thresholds to balance aggressiveness versus accuracy
• Process evaluation workloads in parallel using consumer groups for faster test execution
• Track quality trends over time with message persistence that supports historical analysis and regression detection

Start with the RAG docs to set up your first evaluation pipeline. Build evaluation into your workflow from day one using the RAG Triad framework (context relevance, faithfulness, and answer relevance) across your full pipeline. Check out the observability docs for production monitoring approaches that catch failures before users report them.

Ready to build production-ready RAG evaluation? Try Redis free to test your evaluation infrastructure, or book a demo to see how Redis handles RAG evaluation at scale.