RAG Implementation: Ground Your AI in Business Data
Chunking, access control, evaluation loops, and incident response - how to ship retrieval-augmented generation without silent failures.
RAG grounds model outputs in business documents through retrieval at query time, reducing hallucinations when implemented with discipline.
Core stack
Ingestion/chunking/embedding, retrieval with metadata filters, prompt assembly with citations, and continuous evaluation.
Access control
Retrieval must enforce document ACLs; otherwise RAG becomes a confidentiality risk.
Quality loop
Measure citation precision, groundedness, latency, and refusal behavior on a fixed weekly benchmark set.
Conclusion
Treat RAG as an information architecture and governance problem, not just a model feature.
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