The Hands-on AI Engineer Playbook to Build RAG Apps for Production

RAG latency is dominated by the prefill step, not retrieval. When a model processes 16,000 input tokens, the vast majority are retrieved chunks, and prefill scales quadratically with input length. This deep dive covers how the KV cache works, why prefix caching fails for RAG workloads (near-zero hit rate), three failure modes of naive cache reuse, and six published approaches to fix them — including CacheBlend (2-3x TTFT reduction, recomputing only 10-15% of tokens) and TurboRAG (up to 9.4x TTFT reduction). Production best practices and hands-on implementation are also covered. A secondary section explains outcome reward models vs. process reward models for LLM reasoning verification, with practical guidance on when to use each.