AI

From Logs to Faults: Reference-Free Fault Localization for Retrieval-Augmented Generation Pipelines

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Briefings

RAGLog is an open-source tool that detects and localizes faults in Retrieval-Augmented Generation pipelines using execution logs alone, without requiring reference answers or code instrumentation. The system analyzes approximately five seconds of data per run at a cost of US$0.005, achieving a pooled F1 score of 0.73 on naturally occurring faults and 0.84 on injected faults. This work addresses the challenge of manual fault localization in RAG systems where faults can propagate downstream and remain undetected.

Key points

• RAGLog detects and localizes faults across RAG stages using execution logs without requiring reference answers or code instrumentation.
• The system analyzes approximately five seconds of data per run at a cost of US$0.005, outperforming existing evaluation approaches on naturally occurring faults.

Context

RAG pipelines are widely used for domain-specific question answering, yet locating faults remains manual and time-consuming. Existing evaluation approaches often focus on individual stages and rely on reference answers or code instrumentation, which RAGLog addresses by using execution logs alone.

Limitations

The study uses a live fault injector and a dataset of 3,134 runs from five RAG pipelines. Results are based on a single preprint (arXiv:2611.03725v1) and may not reflect production environments. Performance on injected faults is comparable to existing tools but lacks long-term validation in live production settings.

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