Building Production-Ready AI Document Processing Pipelines with RAG

A battle-tested guide to architecting, implementing, and scaling document intelligence systems that actually work in production After building and operating a RAG system processing 50K+ documents monthly with 99.9% uptime at CarbonFreed, I've learned that successful RAG systems are 20% model selection and 80% systems engineering . This isn't another tutorial about calling OpenAI's API—it's a pragmatic guide to the architectural decisions, failure modes, and operational realities that separate prototypes from production systems. Table of Contents The Systems Thinking Framework Pre-Implementation: The Questions That Matter Architecture: Beyond the Happy Path The Chunking Problem: More Art Than Science Evaluation: What Actually Works Retrieval Strategies: Hybrid is Table Stakes Production Observability: You Can't Fix What You Can't See Cost Engineering: The Reality of Token Economics GraphRAG: When and Why Failure Modes and Debugging Strategies Team Structure and Workflows Decision Framew