Building Production-Ready RAG Applications: Jerry Liu
AI Engineer. LlamaIndex's CEO walking the gap between "naive RAG demo" and a real pipeline — small-to-big retrieval, sub-question routing, hybrid search, evaluation. The shape of his slides maps almost directly onto the article's pipeline sections; watch first, then re-read the article with his diagrams in your head.
AI Expert note
LlamaIndex APIs and recommended components change, but the production gaps are durable: ingestion quality, retrieval routing, reranking, evals and observability. Verify current library defaults before implementing.
What you should get from this
Identify the production RAG controls missing from naive document-chat demos.
Watch or know first
Have built or evaluated a basic RAG prototype.
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