To evaluate a RAG system’s performance over time or after updates, implement a continuous evaluation pipeline that tracks key metrics for both retrieval and generation components. Start by defining a versioned benchmark dataset of input queries, expected retrieved documents, and reference answers. Automate the pipeline to run evaluations after each update, comparing results against historical baselines to detect regressions. Use CI/CD tools to trigger tests automatically and log results for analysis.
For retrieval, measure precision (e.g., percentage of top-k retrieved documents relevant to the query) and recall (ability to retrieve all relevant documents). Track metrics like Mean Reciprocal Rank (MRR) to assess ranking quality. For generation, use metrics like ROUGE or BERTScore to compare generated answers against references, and employ factuality checks (e.g., using an entailment model) to verify if claims align with retrieved content. Include human evaluation for nuanced cases, such as coherence or real-world accuracy, to complement automated scores. For example, if a system update introduces a new embedding model, a drop in MRR could signal retrieval degradation, while a decline in factuality scores might indicate generation issues.
Monitor latency and throughput to ensure updates don’t degrade usability. Use A/B testing in production to compare new and old versions with real user queries. Set thresholds for critical metrics (e.g., “factuality score must not drop below 0.85”) to trigger alerts. For instance, if a model update causes a 10% increase in answer latency or a 15% drop in precision@5, the pipeline flags it for review. Regularly refresh the benchmark dataset to reflect evolving user needs and domain shifts, ensuring evaluations stay relevant. This approach balances automation for scalability with targeted manual checks for complex issues.