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FeedbackAlignment
Analytics
Report block
Measures agreement between AI score results and human feedback using AC1, accuracy, class distribution, and confusion matrices.
What It Answers
- How aligned is this score with human feedback?
- Which labels are being confused?
- Which scores need attention first?
Use When
- Starting score optimization.
- Comparing scorecard-level alignment across scores.
- Checking whether recent rubric or prompt changes improved feedback agreement.
Avoid When
- You need individual contradiction explanations; use FeedbackContradictions.
- You only need marketing-friendly acceptance numbers; use AcceptanceRate.
Run From The CLI
Direct report commands are the simplest path for one-off usage. Saved report configurations use `plexus report config create` and `plexus report run`.
plexus feedback report alignment \
--scorecard "Customer Service QA" \
--score "Medication Review: Dosage" \
--days 30 \
--format jsonplexus report config create --name "FeedbackAlignment Example" --file feedback-alignment.md
plexus report run --config "FeedbackAlignment Example"Minimal Configuration
class: FeedbackAlignment
scorecard: "Customer Service QA"
score: "Medication Review: Dosage"
days: 30Live Rendered Example
Rendered with the same dashboard component used by generated reports.
FeedbackAlignment Example
March 1, 2026 to March 31, 2026
Overall
Agreement
Accuracy
Raw Agreement
96 / 120
96 / 120
Medication Review: Dosage
Agreement
Accuracy
Confusion matrix
Yes
62
8
Yes
No
16
No
34
Predicted
Actual
Raw Agreement
96 / 120
96 / 120
How To Interpret It
- High AC1 means the score and reviewers agree beyond chance.
- Use the confusion matrix to choose whether to focus on false positives or false negatives.
- Low item count should be treated as weak evidence, even if the gauge looks strong.