Production RAG in BFSI — what the architecture must handle
The gap between demo and production
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The real test of a RAG system is not whether it can answer questions. It's whether you can explain why it answered the way it did.
What RBI actually asks for
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Audit log requirements
# Example: Structured audit log entry
audit_entry = {
"timestamp": "2026-05-15T14:23:45Z",
"query_id": "q-abc123",
"user_id": "u-xyz789",
"query_text": "What is the policy on...",
"retrieved_documents": [
{"doc_id": "d-001", "chunk_id": "c-042", "score": 0.89},
{"doc_id": "d-003", "chunk_id": "c-017", "score": 0.85}
],
"response_text": "According to policy document...",
"citations": ["d-001:c-042", "d-003:c-017"],
"model_version": "gpt-4-turbo-2024-04-09",
"latency_ms": 1243
}Citation provenance architecture
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The three layers of provenance
- Document-level: Which source documents contributed to the response
- Chunk-level: Which specific passages were retrieved and used
- Sentence-level: Which parts of the response map to which sources
Evaluation pipelines for regulated environments
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If you cannot measure citation accuracy, you cannot defend your system in an audit.
Building the evaluation suite
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What this means for your architecture
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Related insights
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