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When a pipeline job fails, the AI automatically analyzes the error and provides a plain-English explanation of the root cause and suggested fix. This appears as a separate status entry after the error, making it easy to diagnose issues without reading stack traces.

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How it works

When any pipeline stage (data quality, transformation, destination loading) throws an exception:
  1. The error is logged to status as usual
  2. The error message and pipeline configuration are sent to the AI model
  3. The AI returns a concise explanation of what went wrong and how to fix it
  4. The explanation is written as a separate status entry (AI Explanation: ...) and logged to the pipeline logs
If the AI call itself fails (rate limit, timeout, etc.), the explanation is silently skipped — it never causes a secondary failure.

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Example

Error status: 
Process completed, error: Aborting processing this pipeline, 3 error(s) were found
while performing data quality rules:
Data quality AI failure, row: 2, reason: high (7.50) is less than low (8.01)
Data quality AI failure, row: 3, reason: volume is negative (-500)
Data quality AI failure, row: 4, reason: close and adj_close exceed $1,000,000
AI Explanation status (separate entry): 
AI Explanation: Three rows in the source data violate the data quality rules:
row 2 has a high value lower than low (likely a column swap or data entry error),
row 3 contains a negative volume, and row 4 has close and adj_close values exceeding
$1,000,000. Fix: Correct the upstream source data or add a pre-processing
transformation step to sanitize these issues before the data quality check runs.

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Configuration

No additional configuration is needed. AI error explanation is automatically enabled since an AI provider is required to run the platform.

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Requirements