💡 The paper presents a real-time verification system for retrieval-augmented generation that can process long documents and balance latency constraints with comprehensive answer validation. The problem addressed is that verifying generated answers in retrieval-augmented generation systems is difficult due to the large size of the source materials and the need for interactive services to respond quickly. Large language models can check long contexts but are too slow and costly, while lightweight classifiers operate within strict context limits and frequently miss evidence outside truncated passages.

The method proposed is a real-time verification component integrated into a production retrieval-augmented generation pipeline that enables full-document grounding under latency constraints. The system can process documents up to 32K tokens and employs adaptive inference strategies to balance response time and verification coverage across workloads.

The results show that full-context verification substantially improves detection of unsupported responses compared with truncated validation. The evaluation methodology used to deploy the verifier highlights the importance of long-context verification, the limitations of chunk-based checking in real documents, and the impact of latency budgets on model design. The findings provide practical guidance for practitioners building reliable large-scale retrieval-augmented applications, demonstrating that the proposed system can effectively verify generated answers in real-time while maintaining comprehensive coverage of the source materials.