The Retrieval Checklist I Wish I'd Had Before Shipping RAG

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Actionable checklist for RAG systems, a core data engineering pattern, with high practical value for shipping production systems.

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Why It Matters

For platform and AI engineers building production RAG systems, this checklist directly addresses the most common failure mode—retrieval quality—and provides actionable pipeline decoupling strategies to avoid frozen, stale deployments.

Key Takeaways

Decouple indexing and query paths from day one, and validate chunk quality by reading ten random chunks cold to ensure each is self-contained.

The Retrieval Checklist I Wish I'd Had Before Shipping RAG

Summary

RAG failures stem from retrieval issues 73% of the time, not the LLM. The core architectural mistake is coupling the offline indexing path (chunking, embedding, vector store writes) with the online query path (rewriting, retrieval, reranking, generation), preventing independent iteration. Chunking must produce self-contained units that answer questions alone, and embeddings should include surrounding context like section headers, not just raw body text.

Author

James Anderson