Module 5 of 5 · 60 min

Advanced RAG Capstone: Design, Implement & Evaluate

Architect an end-to-end production RAG system with hybrid search, metadata ACL filtering, cross-encoder reranking, and automated RAG Triad verification.

Core concept

By the end

You will be able to

Build a complete end-to-end RAG architecture meeting latency and groundedness SLAs.
Integrate hybrid BM25 + dense search with cross-encoder reranking.
Execute automated RAG Triad evaluation achieving >= 90% groundedness on test suites.

Production Capstone Specification

In this capstone, you will build a complete enterprise RAG system that ingests heterogeneous documents, indexes them with hierarchical parent-document chunking, applies hybrid search and reranking, and validates output against strict RAG Triad thresholds.

Practice activity

Deliver Production-Grade RAG Pipeline

Implement ingestion pipeline with parent-document chunking.
Deploy hybrid search and cross-encoder reranking stage.
Run evaluation suite and generate machine-readable compliance report.

What to produce

Architecture diagram, pipeline implementation code, and evaluation scorecard JSON.

Reflect before continuing

How does your pipeline ensure enterprise security and context boundary isolation?

Evidence

Sources and verification

Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksArXiv · verified 2026-08-22

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your account transcript.

01In a production RAG system, what is the primary purpose of separating retrieval indexing from context synthesis?ATo maximize semantic matching resolution while providing the LLM complete paragraph context.BTo permanently delete the original source documents from enterprise storage.CTo prevent models from reading system prompts.DTo bypass all user authentication checks.