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Great question! Let me break down RAG (Retrieval-Augmented Generation) and its best practices.
RAG combines the power of large language models with external knowledge retrieval, creating systems that can access up-to-date information beyond their training data.
How RAG Pipelines Work
Document Ingestion — Source documents are chunked into manageable pieces (typically 256-1024 tokens) and converted into vector embeddings.
Vector Storage — Embeddings are stored in a vector database (Pinecone, Weaviate, Qdrant, or pgvector) with metadata for filtering.