Building a Production RAG Pipeline with LlamaIndex and Pinecone

Short summary

Production RAG systems fail at data pipelines, not the LLM—60% of enterprise AI pilots stall due to infrastructure issues. This guide covers LlamaIndex orchestration, Pinecone storage, and production-critical strategies for chunking, metadata management, and filtered retrieval. Key concerns: deduplication, incremental updates, and scoped access controls prevent outages at scale.

• •Most RAG failures occur at data processing and vector storage (steps 2-4), not LLM quality—architecture matters more than model choice
• •LlamaIndex handles ingestion/orchestration; Pinecone persists embeddings; 512-token chunks with 50-token overlap and metadata tags enable precise retrieval
• •Production readiness requires deduplication, incremental indexing, filtered retrieval by metadata (department/access level), and monitoring of retrieval precision

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