Naive RAG fails in production; 7 architectures offer solutions

Naive Retrieval-Augmented Generation (RAG) pipelines often fail in production despite appearing effective during initial demos. These failures stem from five key retrieval issues: vocabulary mismatch, lost context, questions not aligning with answers, answers requiring entire documents, and the system retrieving plausible but incorrect information. To address these, seven distinct RAG architectures have emerged, each targeting specific failure modes. The most fundamental upgrade involves Hybrid Search combined with Reciprocal Rank Fusion and reranking, which should be implemented before exploring more complex methods. AI

IMPACT Addresses common production failures in RAG systems, guiding developers toward more robust architectures beyond basic implementations.

RANK_REASON Article discusses specific technical architectures and methods for improving existing AI tools rather than a new release or fundamental research.

Read on dev.to — LLM tag →

• BM25
• Naive RAG
• reciprocal rank fusion
• retrieval-augmented generation

• product
• infra

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