Lilly Tech Systems
Designing RAG Systems at Scale
Master the architecture, implementation, and production operation of Retrieval-Augmented Generation pipelines. From document ingestion and vector indexing to advanced retrieval strategies and cost optimization — everything you need to build RAG systems that work in the real world.
Your Learning Path
Follow these lessons in order to build a complete production RAG system, or jump to any topic you need right now.
1. RAG Architecture Fundamentals
Why RAG beats fine-tuning for most use cases. Core retriever + generator components, naive vs advanced RAG, and a decision tree for when to use RAG vs fine-tuning.
2. Document Ingestion Pipeline
Chunking strategies (fixed, semantic, recursive), document parsers for PDF/HTML/tables, metadata extraction, and pipeline architecture with Python/LangChain code.
3. Embedding & Vector Indexing
Embedding model selection, vector database comparison (Pinecone, Weaviate, Qdrant, pgvector), indexing strategies (HNSW, IVF), and hybrid search patterns.
4. Advanced Retrieval Strategies
Multi-query retrieval, HyDE, re-ranking with cross-encoders, contextual compression, parent-child document retrieval with production code examples.
5. Generation Pipeline Design
Prompt engineering for RAG, citation and source attribution, hallucination detection, streaming responses, and context window management.
6. RAG Evaluation Framework
Faithfulness, relevancy, context precision/recall metrics. Automated evaluation with RAGAS, A/B testing RAG systems, and regression testing pipelines.
7. Scaling RAG in Production
Multi-tenant architecture, caching strategies, cost optimization with per-query analysis, monitoring retrieval quality, and incremental index updates.
8. Best Practices & Checklist
Production deployment checklist, common failure modes, debugging RAG quality issues, and a comprehensive FAQ for RAG engineers.
What You'll Learn
By the end of this course, you will be able to:
Design RAG Architectures
Architect end-to-end RAG pipelines for production use cases — from document ingestion through retrieval to generation with proper evaluation.
Build Production Pipelines
Implement chunking, embedding, indexing, and retrieval code using Python, LangChain, and real vector databases you can deploy at work tomorrow.
Optimize Cost & Quality
Measure retrieval quality with RAGAS metrics, reduce per-query costs with caching and batching, and debug the most common RAG failure modes.
Scale to Production
Handle multi-tenant workloads, implement incremental index updates, set up monitoring dashboards, and run A/B tests on retrieval strategies.