Rag

This post maps the path from a natural-language query to the final response, showing which components run and in what order. It is the starting point of the series; the rationale behind each component choice and the experiments along the way are covered in the posts that follow. Why routing architecture instead of a single NL2SQL pipeline When NL2SQL is first applied to a real enterprise environment, the same problem usually appears. DDL alone does not let the LLM infer what a table named T_CUST_MST actually means, or what calculation logic “net revenue” refers to in this organization. Benchmark scores keep climbing, yet reports from real enterprise deployments still show accuracy falling below 50%. The same category of errors does not disappear just by swapping datasets. ...

RAG starts from scratch every time. Karpathy proposes having the LLM maintain a wiki directly so knowledge accumulates. DataNexus’s ontology catalog needs the same principle to avoid abandonment.

“What’s Your VIP Criteria?” This happened during a BI Agent project for a retail company. A business user was testing the Agent and asked, “Show me last month’s VIP customer revenue.” The system spit out a number, but the user did not look happy. “Something’s off. I think the VIP criteria are different from what our team uses.” Marketing’s VIP and CRM’s VIP were different. Same with revenue. Depending on whether you meant net revenue (순매출) or gross revenue (총매출), the difference could be hundreds of millions of won. ...