The hard part about analyzing data isn’t generating a a series of analysis steps, it’s proving it’s correct.
Our current approach:
- Sandboxed / sample runs on smaller datasets before full execution
- Step-level transparency: summaries, intermediate tables, and generated code are all visible
- Parallel and sequential test-time execution to surface inconsistencies
- dbt-style pipelines for reproducibility and explicit dependencies
- Decomposing analyses into small, verifiable steps to avoid error compounding (similar to MAKER-style approaches)
- Online validation checks on intermediate and final outputs that trigger re-analysis when assumptions are violated
- A gradually evolving semantic layer to improve consistency and governance over time
Curious how others think about this: what would make you trust an AI-driven data platform?
How do you validate AI-generated data transformations before prod? · HackerTrans
Our current approach: - Sandboxed / sample runs on smaller datasets before full execution - Step-level transparency: summaries, intermediate tables, and generated code are all visible - Parallel and sequential test-time execution to surface inconsistencies - dbt-style pipelines for reproducibility and explicit dependencies - Decomposing analyses into small, verifiable steps to avoid error compounding (similar to MAKER-style approaches) - Online validation checks on intermediate and final outputs that trigger re-analysis when assumptions are violated - A gradually evolving semantic layer to improve consistency and governance over time
Curious how others think about this: what would make you trust an AI-driven data platform?