A RAG Pre-Production Checklist with Real Configuration

Description and use

A step-by-step checklist for preparing RAG for release, covering golden dataset creation, deterministic checks, metric configuration, and CI integration.

Problem

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

Outcome

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.

Workflow

• 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.

Evidence

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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