RESEARCH · RESEARCH · #534
SAGE: Governed LLM pipeline for converting enterprise guidelines into structured artifacts (arXiv:2609.17775v1)
Researchers released SAGE (arXiv:2609.17775v1), a multi-stage LLM pipeline that adds governance to extraction from complex enterprise guideline documents via a shared versioned rule store, schema-validated inter-stage contracts, deterministic structural checks, LLM semantic scoring, consistency filtering, and end-to-end provenance. On 120 documents the authors report reducing turnaround from days to 20–100 minutes, extracting 3,896 rules and producing 812 artifacts for review with a 96% document-level success rate and 3.2% hallucination (versus 15.7% without governance).
KEY POINTS
- Researchers released SAGE (arXiv:2609.17775v1), a multi-stage LLM pipeline that adds governance to extraction from complex enterprise guideline documents via a shared versioned rule store, schema-validated inter-stage contracts, deterministic structural checks, LLM semantic scoring, consistency filtering, and end-to-end provenance.
- On 120 documents the authors report reducing turnaround from days to 20–100 minutes, extracting 3,896 rules and producing 812 artifacts for review with a 96% document-level success rate and 3.2% hallucination (versus 15.7% without governance).
- Adds governance, validation, and provenance to LLM-based extraction workflows, materially reducing manual effort and lowering hallucination rates for enterprise document processing.
WHY IT MATTERS
Adds governance, validation, and provenance to LLM-based extraction workflows, materially reducing manual effort and lowering hallucination rates for enterprise document processing.