civiccc / civiccc/suggestomatic
Quality monitoring
Open
- Dominant language
- Python
- Stars
- 16
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
As we make algorithmic changes, we should make sure that we're not degrading the quality of the recommendations.
Contributor guide
No contributing guide indexed for this repository
Research direction
No files, tests, entry points, metrics, or acceptance criteria are named. Start by surveying the recommendation engine and its existing evaluation or monitoring paths, then clarify which quality measures should be tracked and what behavior counts as degradation. Done should include an agreed monitoring approach and validation that detects quality regressions.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, observability
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100