Source Feed Curation with Regression Validation
Continuously improve an idea-intake feed set, then run repeatable regression passes to confirm pipeline quality before publishing selections.
Overview
Content intelligence pipelines degrade when source feeds drift, break, or become noisy. This workflow adds a controlled source-catalog update step and immediate multi-run validation to prove the intake pipeline still produces quality candidates. It helps the organization expand coverage safely while maintaining trust in automated selection outputs.
How It Works
The AI orchestrator updates the intake OPML source file with newly approved feeds and keeps a known-good source subset, excluding persistently failing feeds. It then runs the full idea pipeline repeatedly (generate, score, promote) and compares run metrics to verify stable behavior across consecutive executions. Selection outputs and IDs are logged so teams can audit what the pipeline is promoting after each source change.
Tools Used
Outcome
The organization gets a repeatable reliability gate for source updates instead of trusting a single green run. Feed expansion can happen faster because quality is verified with measurable intake and selection consistency. Teams reduce the risk of shipping weak or unstable content candidates after upstream source changes.