RAG in Production: A Blueprint That Reduces Hallucinations

RAG is a system. Quality depends on chunking, retrieval, and evaluation, not only the model.

Framework

• Ingestion + metadata normalization
• Chunking by meaning with overlap
• Retrieval + filtering + optional reranking
• Answer formatting with citations
• Evaluation set + regression checks

Pitfalls

• No evaluation set → random quality
• Chunking breaks tables and references
• No citations → low trust and no debugging path

Portfolio deliverables

• RAG pipeline diagram + code skeleton
• Evaluation dataset + rubric
• Monitoring dashboard (retrieval hit rate, citations, latency)