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Live webinarRAGSystem DesignArchitecture
The RAG architecture behind every serious AI product
A quick tour of the architecture powering RAG at companies shipping AI to real users.
Param Harrison
Cofounder, AEOsome.com · Chief Mentor, learnwithparam.com
60 minutes · intermediate
About this session
Why this one matters
Peek inside the RAG systems running at companies shipping AI to real users and you find the same architecture shapes. Multi-stage retrieval. Query rewriting. Rerankers earning their keep. Eval loops watching the whole thing. This session walks that reference architecture piece by piece: what each layer does, why it exists, and how to decide which pieces your system actually needs.
Who should watch
- Engineers comparing their RAG prototype to what production looks like
- Tech leads designing a RAG system for a product launch this quarter
- Teams whose RAG works on demos and misses on long-tail queries
Topics we'll cover
What's on the menu
- The reference RAG architecture, piece by piece
- Query rewriting: why it exists and when to skip it
- Multi-stage retrieval that beats single-vector search
- Rerankers: why they earn their place, and when they do not
- Eval loops that stay honest as the index grows
- How to decide which layers your system actually needs
What you'll walk away with
Leave with a blueprint
- Read any production RAG diagram and name each component on sight
- Design query rewriting that lifts recall without wrecking latency
- Compose multi-stage retrieval with lexical, vector, and rerank layers
- Wire an eval loop that keeps working as your index grows
- Strip the reference architecture down to what your product needs