Reproducibility
Is AI Data Cleaning Reproducible?
A model response alone is not a reproducible data pipeline. Reproducibility comes from validating suggestions, freezing the accepted rules, and applying them with a known deterministic engine to a known input.
By the TidyX team · Updated
Freeze the plan before execution
The AI returns structured suggestions, but the server rejects unknown operations, invalid parameters, unsafe targets, excessive rule counts, and free-form payloads. The user can then accept, skip, or edit only supported controls.
Confirmation makes the plan immutable. Starting a run creates a separate frozen snapshot of enabled rules and a SHA-256 value for that snapshot. Later UI edits cannot silently change the plan behind an existing run.
Record the input, output, and engine identity
A successful run can report its input hash, output hash, rule snapshot hash, engine version, timestamps, and actual summary. Together these values establish which source, instructions, and engine produced the result.
The PDF and JSON audit downloads expose run information in portable form. The selected cleaned format is the data artifact. Generator source for additional scripts remains internal in this phase and is not advertised as a current UI export.
- Validated rule DSL
- Immutable confirmed plan
- Frozen run snapshot and hash
- Recorded engine version and file hashes
Review compatibility before cross-project reuse
You can save a successful run as a recipe and check it against another profiled project. Schema differences may require explicit column mappings before a new draft plan can be created.
A compatible schema does not prove that every business assumption is still valid. Review the new draft and its high-risk rules before accepting a separate execution quote. The new run preserves its own snapshot and provenance.
Frequently asked questions
What makes a cleaning run reproducible?
The confirmed rules are frozen and applied to a known input by a known deterministic engine. Input, output and rule snapshot hashes plus the engine version record the provenance of the run.
Will asking AI again always produce the same plan?
A model response alone is not a reproducible pipeline. Reproducibility applies to executing the frozen rules on the same input with the same engine, rather than relying on a fresh AI response.
Can I reuse a plan on a different file?
A saved recipe can create a new draft after schema compatibility review. Review the new draft, its assumptions and any high-risk rules before accepting a new execution quote.