Retrieval-Augmented Generation is the workhorse pattern of production AI right now, and it's also one of the easiest things to get subtly wrong. A RAG pipeline that performs great in a demo can degrade badly once real documents, real query volume, and real edge cases show up, often without throwing a single error.
This post walks through the parts that actually decide whether a RAG system holds up in production: how you chunk documents, how you pick and commit to an embedding model, what your retrieval pattern actually returns, and where all of this quietly breaks down once you're past the demo stage.
Read the full deep dive on AI Wisdom -> https://www.aiwisdom.dev/articles/ai-engineering/designing-rag-systems-that-actually-scale
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