Evaluating model drift in RAG (Retrieval-Augmented Generation) applications in production is an important task to ensure the ongoing quality, relevance, and safety of the system. Here are some key approaches:
Regular performance monitoring:
- Track key metrics like relevance scores, retrieval accuracy, and generation quality over time.
- Use automated testing frameworks with a curated set of queries to detect changes in output quality.
Data distribution analysis:
- Monitor the distribution of input queries and retrieved passages.
- Look for shifts that could indicate changing user needs or data staleness.
Relevance feedback:
- Collect and analyze user feedback on the relevance of retrieved information.
- Track changes in user satisfaction or task completion rates.
Retriever-specific evaluations:
- Measure retrieval precision and recall on a test set periodically.
- Analyze changes in embedding space or clustering of retrieved documents.
Generator-specific evaluations:
- Use perplexity score or other language model metrics to detect shifts in generation quality.
- Compare generated outputs to reference answers or human evaluations.
End-to-end testing:
- Regularly run a diverse set of queries through the full RAG pipeline. Can be augmented with synthetic data generation.
- Compare outputs to expected results or have human raters evaluate quality.
Data freshness checks:
- Monitor the age of retrieved documents and their last update times.
- Implement alerts for when critical information sources become outdated.
Concept drift detection:
- Use statistical divergence tests to detect shifts in the underlying data distribution.
- Monitor for the emergence of new topics or terminology not well-represented in the training data.
A/B testing:
- Periodically compare the current production model to a baseline or newly trained version.
- Evaluate performance differences to decide if retraining or updates are needed.
External knowledge integration:
- Cross-reference outputs with authoritative external sources if possible.
- Flag discrepancies that could indicate outdated or drifting knowledge.