Building a Production RAG Pipeline: From Ingestion to Verified Source Citation
Moving from an AI prototype notebook to an enterprise RAG system requires document ingestion chunking, hybrid vector/keyword retrieval, strict JSON output validation, and complete data privacy guarantees.
The Anatomy of a Production-Ready RAG Pipeline
Basic RAG fails because naive vector cosine similarity returns noisy or out-of-context chunks. Our production architecture implements a four-stage retrieval pipeline:
Split by Markdown AST headers and semantic paragraphs (500 tokens, 100 token overlap) with document metadata embedding.
Combines pgvector HNSW cosine index queries with PostgreSQL pg_trgm keyword matching via Reciprocal Rank Fusion (RRF).
Re-ranks top 20 retrieved candidates down to top 5 high-relevance chunks using a fast cross-encoder model (e.g. Cohere or BGE reranker).
Injects cited chunks into Claude 3.5 or GPT-4o with deterministic JSON schema validation, guaranteeing exact document citations and preventing hallucinations.
Integrating Private AI into Your Business Software?
We engineer production RAG pipelines, LLM agent workflows, and automated document extraction engines with zero data retention enterprise contracts.