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Rethink RAG: Auditable AI for Regulated Fields
17 Aug
Summary
- RAG systems need auditability beyond demo performance.
- Cascade architecture prioritizes deterministic steps.
- Asymmetric risk prompts are crucial for high stakes.
For high-stakes classification, traditional RAG systems that route all ambiguous cases to language models are insufficient for audits. A cascade architecture offers a more robust solution for regulated environments where decisions must withstand scrutiny long after they are made.
This approach stages processing. Stage one resolves cases deterministically with rules or exact matches, handling the majority of volume and ensuring full explainability. Stage two uses retrieval to gather specific evidence for unresolved cases, focusing on the quality of retrieved context.
Stage three, the LLM call, handles only the remaining genuinely ambiguous cases. This significantly cuts inference costs and improves overall quality. Designing prompts with asymmetric risk, acknowledging the unequal consequences of different error types, is also critical.
Proper evaluation requires separating retrieval quality from final accuracy and oversampling stage-three cases. Building feedback loops to improve the retrieval corpus is essential for continuous enhancement of the system's decision-making capabilities.
