A RAG Pre-Production Checklist with Real Configuration

说明与用途

用于准备 RAG 发布的分步检查表，涵盖黄金数据集创建、确定性检查、指标配置和 CI 集成。

问题

RAG launch checklists often lack concrete, actionable steps tied to actual settings, making it hard to implement and monitor retrieval and generation quality.

结果

Teams can now gate RAG releases with a 20–50 row golden dataset, deterministic rule checks, and metric thresholds, reducing bugs and ensuring consistent quality before production.

工作流

• Build a small golden dataset with canonical inputs, expected outputs, metadata, and a one-sentence reason.
• Add deterministic rule checks (Non-Empty, JSON Schema, Contains, Length Check, Regex Match) as a structural floor.
• Set metric thresholds for Answer Precision (0.8), Faithfulness (0.7), Contextual Recall (0.7) and adjust as needed.
• Create and configure a connector in EvaliQA, mapping actual output, retrieval context, and other paths.
• Run the test plan from CI by calling the EvaliQA API with the test_plan_id, dataset_id, and connector_id, handling error codes and interpreting results before merging.

证据

Build a small golden dataset first

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Add deterministic checks as a structural floor

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Set metric thresholds deliberately

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Connect your RAG endpoint and map the output

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Run the plan from CI

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