Software-Engineering

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. ...

I build DataNexus with several coding agents running at once. Claude Code fixes router logic while Codex fills in test coverage. The workspace was the problem. With one checkout and multiple terminals, agents step on each other’s files. One reinstalls dependencies while another’s build breaks mid-run. I juggled stashes for a while, then gave up and ran them one at a time. Agents I added for parallelism were running serially. ...

I hit 80% on my own 30-question benchmark, but only 56% on BIRD Mini-Dev’s 50 public questions. Nine experiments later, I had ruled out the multi-candidate hypothesis from three different angles. What’s left is schema understanding and methodology.

How product master DB data flows through a 3-stage pipeline to become JSON-LD in your HTML . Covers the pipeline architecture and SSR-based automated deployment.