Ragwarrant controls deployment safety for rag systems under multiple constraints
RAGWarrant: Evidence-Preserving Governance for RAG Policy Promotion Under Quality, Cost, Latency, and Risk Constraints
Artificial IntelligenceComputation and LanguageMachine Learning
Summary
Deploying improvements in retrieval-augmented generation (RAG) systems can be tricky because it’s hard to know if a change is truly safe and beneficial. The authors created RAGWarrant, a system that treats policy updates carefully by checking quality, cost, speed, and risk before allowing a change. It looks at different evidence outputs, blocks risky changes, and keeps detailed records for later review. They tested RAGWarrant on several benchmarks and showed how it can prevent unsafe promotions or choose safer options.
What this means in practice
- •For machine learning operation teams: Control the safe rollout of retrieval-augmented generation system updates while respecting quality, cost, latency, and risk limits.
- •For enterprise ai developers: Use a reproducible, auditable framework to decide when RAG model improvements can be promoted into production environments.
Authors
Richard Krueger, Lucas Krause, Zach Pocquette
Abstract
Retrieval-augmented generation systems are extensively instrumented with metrics, benchmarks, traces, and automated judges, but these tools do not decide whether a proposed policy change is safe to release. We present RAGWarrant, an open-source promotion-control framework that treats deployment as a constrained evidence decision rather than a leaderboard choice. RAGWarrant normalizes evaluator outputs and operational telemetry, applies predeclared quality and hard-risk gates, assigns evidence-class claim ceilings, preserves negative outcomes, and emits auditable PROMOTE, BLOCK, REJECT, or INCONCLUSIVE decisions. We evaluate the framework across T2-RAGBench, MultiHop-RAG, CRAG, HotpotQA, synthetic reproduction, and bounded local generative experiments. On HotpotQA, operational savings were blocked because answer quality fell beyond the declared margin. A bounded CRAG study selected a lower-cost quality-tied policy, but related generative gains were unstable and a held-out guardrail failed closed. We claim an auditable promotion-control abstraction, not optimizer superiority, human validation, or production readiness. The tagged artifact reproduces from a fresh clone, runs as a hardened Docker job, accepts external evaluator exports, and verifies artifact integrity.