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From a prototype that works on slides to a system that holds up with real users.

Param Harrison
Cofounder, AEOsome.com · Chief Mentor, learnwithparam.com
Naive RAG works on slides and breaks the moment real users ask real questions. This walkthrough rebuilds it the way production teams actually do: chunking that preserves meaning, hybrid retrieval, reranking that earns its keep, and evaluation that catches regressions before your users do. You leave with a blueprint you can ship.
Go all the way: bootcamp
Go from software engineer to AI engineer. Build RAG pipelines, agents, and a capstone you can demo.
See the programWorkshop deep-dive
Build production agentic AI systems, from architecture to deployment.
Open courseGrab the companion ebook
The production RAG reference. Chunking, retrieval, reranking, evaluation, failure modes.
Open ebook