What will I learn from The RAG architecture behind every serious AI product?

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

Is The RAG architecture behind every serious AI product live or on-demand?

The RAG architecture behind every serious AI product is available on-demand. The recording is watchable immediately after registration.

How long is The RAG architecture behind every serious AI product?

About 60 minutes including live Q&A.

What do I need before watching The RAG architecture behind every serious AI product?

Comfort with the fundamentals of rag. The webinar walks through real code and real architecture decisions, so a working knowledge of Python or TypeScript helps.

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The RAG architecture behind every serious AI product

Name: The RAG architecture behind every serious AI product
Uploaded: 2026-05-28T14:00:00Z
Duration: 60 min
Description: The reference architecture behind production RAG: query rewriting, multi-stage retrieval, rerankers, evals.

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

Keep going

What to do next

Go all the way: bootcamp

AI Bootcamp for Software Engineers

Go from software engineer to AI engineer. Build RAG pipelines, agents, and a capstone you can demo.

See the program

Workshop deep-dive

RAG Fundamentals for Everyone

Master the core building blocks of RAG, from embeddings to agentic retrieval.

Open course

Grab the companion ebook

The RAG Cheatsheet

The production RAG reference. Chunking, retrieval, reranking, evaluation, failure modes.

Open ebook