After deploying a Retrieval-Augmented Generation (RAG) system, continuous monitoring and debugging are essential to maintain performance and reliability. Key aspects include:
- Tracking retrieval quality: Monitor retrieval metrics like recall and precision to ensure the right documents are fetched.
- Observing generation accuracy: Evaluate LLM outputs for hallucinations, relevance, and grounding.
- Logging and alerting: Implement logging for user queries, retrieved chunks, and generated responses. Set up alerts for anomalies or performance drops.
- A/B testing: Compare different retrieval strategies or prompt templates to optimize.
- User feedback loops: Integrate feedback mechanisms to capture real-world issues.
Common failure modes include outdated or missing documents, embedding drift, and lexical mismatches. Regular evaluation and iterative improvements help keep the RAG system robust.