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AGO AI Quality Gate: Evidence-First Release Decisions for Retrieval-Augmented Generation

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Briefings

AGO AI Quality Gate is an evidence-first framework for industrial RAG assessments that uses a four-state decision model and stratified beta-binomial gates to evaluate LLM judges. The study evaluated gpt-4.1-nano and gpt-4o on RAGBench, finding that low-cost judges detect non-adherent answers at 0.603 AUROC, while gpt-4o reaches 0.783 AUROC. However, gpt-4o's per-domain performance ranges from 0.62 to 0.88, indicating that higher accuracy does not guarantee better domain-specific outcomes. The fixed-seed gate study showed that a low-cost judge reduced unsafe promotion to 22.2%-35.1% compared to 29.3%-41.8% for a naive gate.

Key points

• AGO AI Quality Gate uses a four-state decision model to treat missing data and judge errors as explicit outcomes in RAG assessments.
• Stratified beta-binomial gates quantify regression risk probabilistically, reducing unsafe promotion to 22.2%-35.1% under regression compared to naive gates.

Context

Enterprises adopting retrieval-augmented generation (RAG) face recurring operational decisions to promote, revise, or block system versions. This evidence is incomplete, with metrics coming from fallible LLM judges, necessitating a framework to measure judge quality per engagement.

Limitations

Evidence is limited to proprietary engagement data evaluated on public RAGBench benchmark; results are based on fallible LLM judges with uncertain metrics. Fixed-seed gate study quantifies unsafe promotion costs but lacks broader deployment context.

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