🎬 How to Build a Scalable RAG System for AI Apps (Full Architecture)
Video Details
Channel: ByteMonk URL: https://www.youtube.com/watch?v=4KiiKQ9RVvA Relevance: ⭐⭐⭐⭐⭐
Summary
Comprehensive architectural guide for production RAG systems covering chunking strategies, embedding model selection, vector store configuration (Qdrant vs ChromaDB vs Pinecone), retrieval augmentation patterns, and latency optimisation. Includes a full code walkthrough with performance benchmarks.
PUMA Relevance
Primary reference for PUMA Stage 4 RAG pipeline design. The chunking strategy section informs how PUMA splits Jira issue text (title + description together, no splitting). The Qdrant configuration guide is directly used in PUMA’s Docker Compose stack. The latency benchmarks help validate PUMA’s <30s inference target per issue.