Advanced3 hours· 8 lessons

Building RAG Applications

Build RAG systems that actually work in production. This path goes deep on the entire RAG pipeline — document processing, chunking strategies, embedding selection, vector storage, retrieval optimization, reranking, and evaluation — with practical guidance from production deployments.

What You'll Learn

  • Understand the complete RAG architecture and pipeline
  • Implement effective document chunking strategies
  • Choose and deploy the right embedding model
  • Set up and optimize vector databases
  • Implement reranking and hybrid search for better retrieval
  • Build evaluation frameworks for RAG quality
  • Handle production challenges including scaling and monitoring

Course Lessons

1

RAG Architecture Deep Dive

22 min read

Understand the complete RAG pipeline from document ingestion to response generation, including common architectures and design patterns.

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2

Document Processing and Chunking Strategies

22 min read

Master document parsing and chunking — fixed-size, semantic, recursive, and document-aware strategies for different content types.

3

Embedding Models: Selection and Deployment

20 min read

Compare embedding models, understand dimensionality tradeoffs, and deploy embedding pipelines that balance quality and cost.

4

Vector Databases in Production

25 min read

Set up Pinecone, Weaviate, Qdrant, or pgvector for production use. Learn indexing strategies, filtering, and performance optimization.

5

Advanced Retrieval: Hybrid Search and Reranking

22 min read

Go beyond basic vector search with hybrid retrieval, cross-encoder reranking, and multi-stage retrieval pipelines for better accuracy.

6

Prompt Engineering for RAG

18 min read

Design prompts that effectively use retrieved context — handling conflicting information, citing sources, and admitting knowledge gaps.

7

RAG Evaluation and Monitoring

20 min read

Build evaluation frameworks measuring retrieval quality, answer accuracy, and faithfulness. Monitor RAG systems in production.

8

Scaling RAG to Production

18 min read

Handle production challenges including index updates, caching, cost optimization, and serving RAG at enterprise scale.

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