AGO AI Quality Gate: Evidence-First Release Decisions for Retrieval-Augmented Generation
AGO Quality Gate uses judge reliability and probabilistic risk to gate RAG release decisions.
AGO AI Quality Gate is an evidence-first framework for promote, revise, or block decisions on retrieval-augmented generation releases. It combines a four-state decision model, layered deterministic and LLM scoring, a stratified beta-binomial regression gate, and mandatory meta-evaluation of the judge. On RAGBench samples of 1,200 traces, gpt-4.1-nano reaches only AUROC 0.603 for non-adherent answers, while gpt-4o reaches 0.783 with per-domain scores from 0.62 to 0.88. Under regression, the decision-grade profile reduces unsafe promotion to 22.2%-35.1%, versus 29.3%-41.8% for a naive gate.
- Four-state decisions treat missing data and judge errors as outcomes.
- gpt-4.1-nano scores non-adherence at AUROC 0.603 versus 0.783 for gpt-4o.
- gpt-4o per-domain AUROC still ranges from 0.62 to 0.88.
- A decision-grade gate cuts unsafe promotion to 22.2%-35.1% under regression.
- Judge quality must be measured per engagement, not by point estimates alone.
- Vendors
- OpenAI
- Products
- AGO AI Quality GateRAGBench
- AI models
- GPT-4.1-nanoGPT-4o
From the original article
1 min read at huggingface.co
Enterprises adopting retrieval-augmented generation (RAG) face a recurring operational decision: promote, revise, or block a system version. The evidence is incomplete and the metrics come from fallible LLM judges. We report on AGO AI Quality Gate (AGO), an evidence-first quality-gate framework deployed in industrial RAG assessment engagements.
By Giulio Zeloni, Enrico Lo Conte, Salvatore Rionero · huggingface.co
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