At a time when AI systems are taking on more and more responsibility, the systematic Evaluation of Retrieval Augmented Generation (RAG) systems the decisive success factor. Because a RAG system is only as good as its weakest level – and without measurement, quality remains a matter of luck.
Why RAG systems need to be measured
If the system provides false or invented answers, trust suffers – and in the worst case, decisions are made on the wrong basis. A well-founded evaluation reveals weaknesses before they cause damage in productive operation.
Two levels: Retrieval and Generation
An RAG system has two stages, which are measured separately: the Retrieval (Does the system find the right sources?) and the Generation (Does it formulate a correct, proven answer?).
| Metrics | What it measures | Example target value |
|---|---|---|
| Context Precision | Percentage of relevant accessed passages | ≥ 0,85 |
| Context recall | Coverage of the necessary information | ≥ 0,90 |
| Faithfulness | Response covered by sources (no hallucination) | ≥ 0,95 |
| Answer Relevance | The answer is the question asked | ≥ 0,90 |
| Latency (P95) | Response time under load | < 3 s |
An example from practice
We evaluate with a fixed “Golden Dataset” from real questions and tested target answers. In one project, the Context recall over 100 test questions for optimization of chunking and retrievers of 0.72 to 0.91 – the hallucination rate decreased accordingly, without the response time suffering.
Our key criteria
- Data quality & retrieval accuracy: relevance and precision with precision, recall and F1 score
- Integration of retrieval and generation: seamless module communication, analysis of latency
- Response quality & contextual understanding: consistent, proven answers, resolution of ambiguities
- Scalability & efficiency: stable performance even with increasing data volume
Evaluation is not a one-time project
Data, models and requirements are changing. Therefore, we anchor evaluation as continuous processGolden Dataset, automated regression testing for each change and operational monitoring. In this way, quality remains measurable – and improves step by step.
At ALGEBRA, we have the methods and tools to make your RAG system reliable. More on our side AI Consulting.
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