Retrieval-augmented generation (RAG) combines retrieval models with generative capabilities to produce more accurate and factual responses, significantly reducing hallucinations. Effective data processing is crucial for RAG systems, as poor document ingestion or inadequate parsing can degrade retrieval quality. Evaluating potential data sources requires assessing their authority, relevance, and domain coverage. Information density, embedding models, noise reduction, metadata extraction, and vector optimization are also essential considerations. Chunking techniques, including semantic similarity chunking and overlap methods, enhance retrieval quality by ensuring context continuity between chunks. Choosing the right vector database is critical for optimizing RAG systems, with various databases offering different performance characteristics and feature sets that match specific use cases. Proper data handling is the cornerstone of effective RAG system performance, and tools like Galileo can help overcome challenges such as incomplete records and outdated information.