This talk provides a practical framework for improving Retrieval Augmented Generation (RAG) systems by focusing on outcomes, analyzing failures, and applying techniques based on complexity-adjusted impact. It covers foundational retrieval methods like BM25 and embeddings, advanced ranking with cross-encoders, incorporating non-relevance signals and user preferences, and strategies for LLM query orchestration. The speaker also discusses cost optimization through model distillation and highlights the critical role of product design and an empirical approach in building robust RAG applications.