---
id: troubleshooting.noisy-ai-suggestions
title: Noisy AI Suggestions
summary: Triage generated suggestions that are fluent but not ready for the artifact they affect.
kind: error
product_area: troubleshooting
status: stable
updated: 2026-09-07
canonical: /docs/troubleshooting/noisy-ai-suggestions
---

# Noisy AI Suggestions

## Symptom

AI-assisted suggestions look polished, but reviewers cannot safely use them for the artifact they affect. The suggestion may sound plausible while missing source evidence, applying too broadly, duplicating an existing standard, or skipping the relevant state boundary.

For policies, rubrics, classifications, and review context, the risk is usually premature approval. For Comparison Directions, the risk is different: AI-suggested directions are active rows, so a noisy suggestion should be edited, archived, deleted, or reviewed through the normal direction controls rather than routed through a separate approval queue.

## Likely causes

- The suggestion was generated without enough case context, reviewer rationale, or source material.
- The suggestion collapses policy, applicability, and rubric into one fluent paragraph.
- Existing standards already cover the behavior, but the suggestion does not detect overlap.
- The artifact is still draft or suggested, but a downstream page treats it as approved.
- An AI-suggested Comparison Direction overlaps user-defined directions or no longer matches current project learning.

## Diagnostic checks

1. Open the source cases, outputs, reviewer notes, or interview answers that motivated the suggestion.
2. Check whether the suggestion names a policy rule, an applicability boundary, or a rubric check clearly.
3. Compare it with existing approved policies and rubrics for overlap or contradiction.
4. For Comparison Directions, compare it with active and archived directions before deciding whether to keep it.
5. Confirm whether the artifact is draft, suggested, approved, rejected, stale, superseded, active, archived, or dismissed.

## Fix

For policies, rubrics, classifications, and review context, keep the suggestion as draft context until a human owner edits, rejects, or approves the specific artifact. Split broad suggestions into policy, applicability, and rubric material before approval. If a benchmark or review note already used the suggestion as governed evidence, correct the evidence note and rerun only after the approved artifact boundary is clear.

For Comparison Directions, handle the suggestion as a normal active direction. Edit it if the boundary is useful but phrased poorly, archive or delete it if it is not useful, and dismiss **Potentially stale** only when the team decides the direction still applies.

## Prevention

Require source cases and reviewer rationale before promoting generated text. Keep suggested and approved states visually and procedurally separate for standards. Review overlap with existing standards before accepting a generated policy or rubric, and review overlap with active or archived directions before keeping an AI-suggested Comparison Direction.

{% example-demo title="Noisy AI Suggestions boundary" %}
Visible problem: An AI-assisted draft says support answers should "be careful with refunds."

Check: The reviewer opens the cases and sees the real issue is unsupported refund exceptions.

Correction: The team rewrites the draft into a policy about approved refund support, applicability for refund requests, and a rubric that fails unsupported exception promises.

Outcome: The draft becomes useful preparation, but benchmark evidence waits for human approval.
{% /example-demo %}

{% example-demo title="Example: noisy Comparison Direction" %}
Visible problem: Teammately suggests a direction for "more cautious answers," but the project already has a user-created direction for unsupported-claim refusal.

Check: The owner compares the AI-suggested row with current and archived Comparison Directions.

Correction: The owner archives the overlapping AI direction or edits it into a narrower boundary.

Outcome: No approval queue is needed. The direction list stays useful, and generated cases still need normal review before benchmark use.
{% /example-demo %}

## Related task pages

{% related-card-grid title="Related task pages" %}
- [What AI Features Can and Cannot Do](/docs/governance/what-ai-features-can-and-cannot-do)
- [Human Approval Boundaries](/docs/governance/human-approval-boundaries)
- [Comparison Directions](/docs/assets/comparison-directions)
- [Editing Suggested Rubrics](/docs/correctness-governance/policies-and-rubrics)
- [Product quickstart](/docs/quickstart)
- [Task index](/docs/operating-manual/task-index)
{% /related-card-grid %}

## Related reference pages

{% related-card-grid title="Related reference pages" %}
- [Policies](/docs/object-model/policies)
- [Rubrics](/docs/object-model/rubrics)
- [Applicability logic](/docs/object-model/applicability-logic)
- [Cases](/docs/object-model/cases)
{% /related-card-grid %}

## Source confidence

Code-backed: Dimension proposals, synthesized Case cards, Comparison Direction controls, and Policy approval expose distinct generated-artifact lifecycles. The diagnostic advice follows those visible boundaries; it does not claim that every AI-assisted surface uses the same approval state.