Phase 3: Enterprise RAG Systems · 60 min · Python · OpenAI SDK · Pinecone

RAG Pipeline End-to-End

The offline path can be slow. The online path must be fast.

What you will learn

Build the offline path: ingestion, parsing, chunking, embedding, indexing
Build the online path: query, retrieval, reranking, context assembly, generation within 1-5 seconds
Choose chunking strategy: semantic chunking at 512-1024 tokens outperforms fixed chunking by 15-25%
Select and version embedding models: OpenAI text-embedding-3, Cohere embed-v3, Voyage AI
Manage context assembly: how much context, ordering, token budget, citation tracking

Objective

What you'll learn

This lesson takes approximately 60 min. By the end, you will be able to:

Build the offline path: ingestion, parsing, chunking, embedding, indexing
Build the online path: query, retrieval, reranking, context assembly, generation within 1-5 seconds
Choose chunking strategy: semantic chunking at 512-1024 tokens outperforms fixed chunking by 15-25%
Select and version embedding models: OpenAI text-embedding-3, Cohere embed-v3, Voyage AI
Manage context assembly: how much context, ordering, token budget, citation tracking

The Problem

Building a RAG pipeline that works on 100 documents is a weekend project. Building one that works on 50,000 enterprise documents with access control, hybrid search, and sub-second latency takes weeks. This lesson walks through the full RAG pipeline: document processing, chunking, embedding, vector storage, retrieval, reranking, and generation.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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