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Building Production-Ready RAG Applications: Jerry Liu — AI Expert OÜ

Building Production-Ready RAG Applications: Jerry Liu

19 minutesAdvancedAI for BusinessUseful with contextTool UI may have changedReviewed May 18, 2026

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.

Practical next step

Watch with the companion article open, then write down one workflow or decision this changes for your own work.

Watch next

Continue through the same learning path with the next curated companion videos.

Further reading

A production RAG pipeline is six stages, each with specific patterns that determine quality. The architecture, the choices at each stage, and the iterative evaluation discipline that distinguishes RAG that works from RAG that disappoints.

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Building a production RAG: ingestion, embedding, retrieval, reranking, eval

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