This document explores the key challenges and practical solutions for improving real-world Retrieval-Augmented Generation (RAG) systems. RAG systems combine data retrieval with language generation, but they face significant issues like missing content, context mismatches, and retrieval inaccuracies, leading to hallucinations and incomplete responses. Solutions include advanced data cleaning, improved prompting, agentic RAG models with live search, and refined retrieval techniques, such as hyperparameter tuning, chunking strategies, and enhanced embedding models. Additionally, it covers corrective approaches like multi-query retrieval, context compression, reranking, and recent research-driven methods for reducing hallucinations and achieving higher response specificity. The document emphasizes future directions with agentic RAG systems and self-reflective models, paving the way for robust RAG deployments in high-stakes applications.