I’m the builder of GlobalDataTracker.com, a free country-statistics site built with Codex/Astra. This write-up also uses AI assistance. A reader challenged a life-expectancy comparison, and investigating it exposed a problem that checking numeric values alone would miss. The World Bank values for Germany were about 81.29 years under the label 2019 and 80.79 under 2024. Subtraction gives −0.5. But that does not establish a clean comparison between those two calendar years. The World Bank API response with footnotes says Germany’s male and female observations labelled 2024 cover 2022–2024 . The total observation has no direct footnote. Separately, Germany-specific source metadata says the total is derived from those sex-specific estimates. The pipeline had kept the values and year labels, but had not requested or preserved the observation notes. The fix was to: Request the documented footnote=y option and retain notes through ingestion, charts and CSV exports. Keep provider footnotes separate from our editorial explanation of related-series metadata. Verify the refreshed dataset: 18,290 provider notes retained; all 287,760 previous numeric observations unchanged and none missing. There are still limits: we haven’t established Germany’s exact 2019 reference period, and the sex series flag an unexplained break in 2023. An empty footnote is not evidence of perfect comparability. For AI-assisted data projects, my takeaway is to review the data contract beyond value and year : units, reference periods, footnotes and series breaks need to survive every transformation too. Implementation example: GlobalDataTracker.com . submitted by /u/TrekkingAround10
Originally posted by u/TrekkingAround10 on r/ArtificialInteligence