Code that runs is not code that's correct: 4 ways to trust an AI-written data pipeline
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
AI-generated data pipelines can run without errors yet still produce wrong results. Four practical habits help build trustworthy pipelines: using an MCP to let the AI agent inspect real data before writing transforms, encoding data contracts explicitly in SQL (unit conversions, QA filters, idempotent writes), generating unit tests from realistic fixture data the agent has seen, and keeping tests local with DuckDB for fast, cheap feedback loops. A concrete GHCN weather data pipeline illustrates each technique.
437 Impressions