How to test your RAG pipeline (before and after you ship)
Blog post from Braintrust
Effective RAG pipeline testing requires instrumenting retrieval and generation as separate trace spans, using production query logs to create a versioned golden dataset, and recording all corpus, model, prompt, and retrieval configuration details for reproducibility. The process evaluates retrieval independently through document-label metrics such as Recall@k, Precision@k, and NDCG or semantic context scorers, then tests generation against fixed reviewed context for faithfulness, relevance, and correctness while validating LLM judges against human review. Full end-to-end experiments should compare one configuration change at a time with a saved baseline, examine individual regressions alongside aggregate scores, use repeated trials for variable outputs, and apply predefined release thresholds, must-pass cases, and handling requirements for unanswerable queries. These checks can be automated in CI with smaller pull-request smoke tests and full post-merge suites, including custom logic that fails builds when quality criteria are missed. After deployment, asynchronous production scoring, dashboards, alerts, metadata filtering, and review of low-scoring traces help detect emerging failures and feed confirmed cases back into the dataset, creating a continuous evaluation cycle supported by Braintrust’s experiments, datasets, tracing, scoring, CI integrations, and production monitoring tools.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.