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.
Hiring signal: Take-home projects at OpenAI and Anthropic test end-to-end RAG pipeline construction. Candidates who separate offline (ingestion/indexing) and online (query/generation) paths, choose chunking strategy deliberately, and version their embeddings pass. Candidates who build a single-path notebook demo fail. The 1-5 second online path SLO is a production constraint that separates FDEs from researchers.
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
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 Offline Path: Ingestion, Chunking, Embedding, Indexing, The Online Path: Query → Retrieval → Reranking → Generation (1-5s SLO), Choosing and Versioning Embedding Models, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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