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
RAG Architecture Deep Dive
22 min readUnderstand the complete RAG pipeline from document ingestion to response generation, including common architectures and design patterns.
Read lesson →Document Processing and Chunking Strategies
22 min readMaster document parsing and chunking — fixed-size, semantic, recursive, and document-aware strategies for different content types.
Embedding Models: Selection and Deployment
20 min readCompare embedding models, understand dimensionality tradeoffs, and deploy embedding pipelines that balance quality and cost.
Vector Databases in Production
25 min readSet up Pinecone, Weaviate, Qdrant, or pgvector for production use. Learn indexing strategies, filtering, and performance optimization.
Advanced Retrieval: Hybrid Search and Reranking
22 min readGo beyond basic vector search with hybrid retrieval, cross-encoder reranking, and multi-stage retrieval pipelines for better accuracy.
Prompt Engineering for RAG
18 min readDesign prompts that effectively use retrieved context — handling conflicting information, citing sources, and admitting knowledge gaps.
RAG Evaluation and Monitoring
20 min readBuild evaluation frameworks measuring retrieval quality, answer accuracy, and faithfulness. Monitor RAG systems in production.
Scaling RAG to Production
18 min readHandle production challenges including index updates, caching, cost optimization, and serving RAG at enterprise scale.
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