Building Reliable RAG Pipelines: From Prototype to Production
The article states that most teams can get a RAG pipeline working in a notebook over a weekend, but very few achieve reliable production performance. The author attributes this gap to engineering discipline rather than model quality. In production, users ask unexpected questions, retrieval degrades as the corpus grows, and models confidently synthesize wrong answers from bad context without instrumentation to catch errors. The article emphasizes that poor chunking cannot be compensated downstream; if relevant information is split across chunks or diluted into one too large, no retrieval algorithm can recover it. The recommended production-grade pattern is hierarchical chunking: maintain parent chunks (full sections) and child chunks (sentences or short paragraphs). Retrieve at child granularity for precision, but return parent text as LLM context for completeness. Every chunk must carry metadata including source document ID, version, content hash, and embedding model version. The content hash indicates when a chunk needs re-embedding due to source changes. The article asserts that neither BM25 nor vector search alone is sufficient; hybrid retrieval with Reciprocal Rank Fusion (RRF) is the baseline for production RAG.
Without engineering discipline in chunking, metadata, and retrieval, production RAG pipelines degrade silently.