Teammately Docs
Docs menu

error

Noisy AI Suggestions

Triage generated suggestions that are fluent but not ready for the artifact they affect.

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 Confirm whether the artifact is draft, suggested, approved, rejected, stale, superseded, active, archived, or dismissed.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.

Worked example

Noisy AI Suggestions boundary

01

Start

Behavior input

Visible problem
An AI-assisted draft says support answers should "be careful with refunds."
02

Middle

Judgment into standard

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.
03

Result

Interpretation

Outcome
The draft becomes useful preparation, but benchmark evidence waits for human approval.

Worked example

Example: noisy Comparison Direction

01

Start

Behavior input

Visible problem
Teammately suggests a direction for "more cautious answers," but the project already has a user-created direction for unsupported-claim refusal.
02

Middle

Judgment into standard

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.
03

Result

Interpretation

Outcome
No approval queue is needed. The direction list stays useful, and generated cases still need normal review before benchmark use.

Related task pages

Reference / Governance and ReproducibilityWhat AI Features Can and Cannot DoExplain the difference between AI-assisted suggestions and approved correctness infrastructure.Reference / Governance and ReproducibilityHuman Approval BoundariesDefine which AI-assisted suggestions require accountable human review before becoming standards.Reference / AssetsComparison DirectionsCreate reusable guidance for meaningful candidate-output differences in comparative expert work.Reference / Correctness GovernancePolicies and RubricsUnderstand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance.Quickstart / Learning ManualProduct quickstartConfigure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.Concept / Operating ManualTask indexRoute correctness work to the current project foundation, benchmark workspace, evaluation, or improvement surface.

Related reference pages

Reference / Object ModelPoliciesDefine policies as reusable statements of what correct AI behavior requires.Reference / Object ModelRubricsTurn policies into binary, reviewable checks that explain pass and fail evidence.Reference / Object ModelApplicability logicExplain when a policy or rubric should be used for a case, output, or coverage segment.Reference / Object ModelCasesUnderstand cases as the situations Teammately uses to represent important AI behavior.

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.

Found something unclear?

Report outdated, unsupported, or confusing docs so we can fix the source page.

Report a docs issue

Continue learning

Related docs

Governance and Reproducibility What AI Features Can and Cannot Do Explain the difference between AI-assisted suggestions and approved correctness infrastructure. Governance and Reproducibility Human Approval Boundaries Define which AI-assisted suggestions require accountable human review before becoming standards. Assets Comparison Directions Create reusable guidance for meaningful candidate-output differences in comparative expert work. Expert Contributions Contributed Artifacts Inspect policies, rubrics, cases, and coverage observations produced through attributable expert contribution work.
AI context