# What AI Features Can and Cannot Do Generated: 2026-09-13T04:42:55.223Z Source build: local Canonical docs: https://teammately.ai/docs --- id: governance.ai-feature-boundaries title: What AI Features Can and Cannot Do summary: Explain the difference between AI-assisted suggestions and approved correctness infrastructure. kind: reference product_area: governance status: stable updated: 2026-09-07 canonical: /docs/governance/what-ai-features-can-and-cannot-do --- # What AI Features Can and Cannot Do ## Definition AI-assisted features can help prepare correctness work, but they do not own the approval boundary. They may draft, classify, summarize, propose, or organize artifacts; a human still needs to approve governed standards and review context before benchmark evidence depends on them. Use this page when a reader needs to separate preparation from authority. A fluent suggestion can be useful, but it is not an approved Policy, Rubric, Case-scoped reference output, Benchmark evidence, or downstream customer decision by itself. > Human approval boundary > > AI features can draft, summarize, classify, or suggest; they do not approve policies, rubrics, benchmark evidence, or customer-owned decisions by themselves. ## Fields, states, or lifecycle rules - AI-assisted output can be draft material, review support, classification help, or summarization. - AI-assisted output should not be treated as approved standards, benchmark evidence, or customer-owned decisions without human approval. - Suggested policies, rubrics, and classifications need visible approval or rejection state before they affect governed evidence. - AI-suggested Comparison Directions are different from suggested standards: they can become active directions immediately, but they still do not approve policies, rubrics, cases, benchmark membership, or review context. - AI-assistance language does not establish model-provider behavior, data retention, compliance posture, or autonomous approval. - Source-backed product pages decide exact supported behavior; this page defines the public boundary. ## Related objects Read this with [Human Approval Boundaries](/docs/governance/human-approval-boundaries), [Comparison Directions](/docs/assets/comparison-directions), [Using Expert Judgment](/docs/concepts/correctness-elicitation), [Using Checkpoints](/docs/expert-contributions/complete-contribution), and [Noisy AI Suggestions](/docs/troubleshooting/noisy-ai-suggestions). {% example-demo title="What AI Features Can and Cannot Do boundary" %} Draft suggestion: An AI-assisted workflow proposes a new compatibility policy after reading rejected cases. Allowed use: The suggestion can help a reviewer start the policy draft. Not allowed as governed evidence: A benchmark should not cite the policy until an accountable human approves the artifact and its applicability. Interpretation: The AI feature accelerated preparation; the human approval boundary still controls whether the standard can govern benchmark evidence. {% /example-demo %} {% example-demo title="Example: active AI-suggested direction" %} AI-suggested direction: Teammately suggests a stale-source conflict Comparison Direction after project cases and ontology values show that gap. Allowed use: The direction can become active immediately and can guide preview examples or future variant generation. Not allowed as governed evidence: The direction does not approve a policy, rubric, generated case, benchmark membership, or downstream decision. Users still review generated examples and manage the direction normally. {% /example-demo %} ## Source confidence Code-backed: generation surfaces can propose Dimension schemas, synthesize candidate Cases, and suggest Comparison Directions; Policy approval and contributed-artifact state establish separate human-governance boundaries. These sources support the product-state distinction here, not claims about model providers, retention, or autonomous authority. ## Related task pages {% related-card-grid title="Related task pages" %} - [Human Approval Boundaries](/docs/governance/human-approval-boundaries) - [Comparison Directions](/docs/assets/comparison-directions) - [Using Expert Judgment](/docs/concepts/correctness-elicitation) - [Using Checkpoints](/docs/expert-contributions/complete-contribution) - [Product quickstart](/docs/quickstart) - [Task index](/docs/operating-manual/task-index) {% /related-card-grid %} --- id: governance.human-approval-boundaries title: Human Approval Boundaries summary: Define which AI-assisted suggestions require accountable human review before becoming standards. kind: reference product_area: governance status: stable updated: 2026-09-07 canonical: /docs/governance/human-approval-boundaries --- # Human Approval Boundaries ## Definition Human approval boundaries separate preparation from governed correctness evidence. A suggestion, reviewer comment, interview answer, or draft standard can inform the loop, but it should not govern benchmark interpretation until the relevant human approval state is clear. Use this page when prepared or contributed material is about to become a governed Policy, Rubric, Case-scoped reference output, or selected Benchmark Dataset evidence. Comparison Directions are a narrower configuration object, not a governed standard. An AI-suggested Comparison Direction can be active without a separate approval step, but generated Cases, Benchmark membership, Policies, Rubrics, and reference outputs still follow their owning review or approval boundaries. A customer-owned human review packet is assembled from evidence; it is not a Teammately approval state. > Approval is a state transition > > AI-assisted suggestions do not become governed standards until an accountable human approves the relevant artifact. ## Fields, states, or lifecycle rules - Draft suggestions and reviewer notes are preparation material. - Approved Policies and Rubrics, reviewed Case changes, selected Dataset membership, and supported Case-scoped reference outputs can affect governed evidence. - AI-suggested Comparison Directions can affect future variant generation as active directions, but they do not approve the generated cases or standards they help explore. - Approval should name the artifact being approved, not only the discussion that produced it. - Stale or superseded approvals should be visible before older benchmark evidence is reused. - This page does not claim external compliance approval, legal signoff, or production deployment authorization. ## Related objects Read this with [What AI Features Can and Cannot Do](/docs/governance/what-ai-features-can-and-cannot-do), [Comparison Directions](/docs/assets/comparison-directions), [Approving Suggested Policies](/docs/correctness-governance/policies-and-rubrics), [Editing Suggested Rubrics](/docs/correctness-governance/policies-and-rubrics), and [Approval History and Reviewer Activity](/docs/governance/approval-history-and-reviewer-activity). {% example-demo title="Human Approval Boundaries boundary" %} Reviewer context: Several experts reject unsupported refund exceptions. Suggested policy: The system proposes a policy that exceptions require approved support. Approval boundary: The suggestion becomes governed only when a human owner approves the policy and its applicability. Benchmark interpretation: Runs should cite the approved policy, not the unapproved suggestion that preceded it. {% /example-demo %} ## Source confidence Code-backed: Policy approval and activity components expose accountable approval state and history; contributed-artifact types keep expert learning distinct from materialized governed objects. Comparison Directions deliberately use active, archived, and advisory-stale behavior instead of the Policy approval lifecycle. ## 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) - [Comparison Directions](/docs/assets/comparison-directions) - [Review Policies and Rubrics](/docs/correctness-governance/policies-and-rubrics) - [Product quickstart](/docs/quickstart) - [Task index](/docs/operating-manual/task-index) {% /related-card-grid %} --- id: assets.comparison-directions title: Comparison Directions summary: Create reusable guidance for meaningful candidate-output differences in comparative expert work. kind: reference product_area: assets status: stable updated: 2026-09-07 canonical: /docs/assets/comparison-directions --- # Comparison Directions ## Definition A Comparison Direction is a reusable project Asset that describes how candidate outputs should differ during comparative expert work. It can focus attention on a meaningful contrast such as evidence grounding, uncertainty handling, or response strategy without declaring which candidate is correct. Comparison Directions are project-scoped. A Contribution can select or allow a pool of directions for its comparative component, while the benchmark Contribution still owns the objective, cases, candidates, and expert task. ## Fields, states, or lifecycle rules - A direction has a name or label and a description of the intended contrast. - Users can create, edit, pin, archive, and remove directions from **Assets → Comparison Directions**. - Pinned directions are surfaced when a Contribution request selects comparative output guidance. - The suggestion experience creates draft candidates in a suggestion run. Nothing enters the reusable library until a user accepts it. - A direction can carry a staleness advisory when its source context has changed. Dismissing that advisory records a review decision; it does not approve a Policy, Rubric, Case, or Benchmark. - A direction guides comparative presentation or generation. It does not create a Case, change coverage structure, or replace expert judgment. ## Correct scope Use Dimensions, Project Topics, and Case Construction Patterns for the behavior space a benchmark should represent. Use Comparison Directions for how candidate outputs should be contrasted within a comparative Contribution. Use Correctness Governance for the approved standard that determines how an output is judged. {% example-demo title="Example: source-grounding contrast" %} A project creates one Comparison Direction asking for a response that cites the current source conservatively and another asking for a focused clarification when the source hierarchy is unresolved. A comparative Contribution can use those directions to elicit an expert preference. The direction does not approve either response or create the governing rubric. {% /example-demo %} ## Source confidence Code-backed: the active Assets routes expose the Comparison Directions library, detail controls, suggestion runs, accept or dismiss decisions, pinning, and staleness review. The API keeps legacy compatibility names internally, but this page uses the current product label. ## Related task pages {% related-card-grid title="Related task pages" %} - [Assets](/docs/assets) - [Request an Expert Contribution](/docs/expert-contributions/request-contribution) - [Manage benchmark coverage](/docs/coverage-management) {% /related-card-grid %} --- id: concepts.correctness-elicitation title: Correctness Elicitation summary: Turn tacit specialist judgment into attributable contributions, governed policies, applicability conditions, and binary rubrics. kind: concept product_area: correctness_elicitation status: stable updated: 2026-09-07 canonical: /docs/concepts/correctness-elicitation --- # Correctness Elicitation Correctness Elicitation is the capability for turning specialist judgment into explicit, reusable correctness specifications. It handles the parts of AI behavior that cannot be settled by a generic score: domain preferences, exceptions, conflicts between sources, applicability boundaries, unacceptable failure modes, and the evidence an expert needs before making a decision. ## Definition Elicitation begins before the expert opens a task. Teammately agents can organize relevant Reference Materials, cases, candidate responses, possible policies, rubric questions, and unresolved conflicts into a focused Contribution. The expert then works through forms, chat, interviews, case review, or checkpoints according to the requested components. The contribution remains attributable. Accepted learning can be materialized into policies, rubrics, cases, or coverage observations, while drafts and unresolved statements keep their own state. Correctness Governance owns the resulting policies and rubrics; Expert Contributions owns the benchmark-scoped human work that produced them. ## Decision checkpoint | Need | Elicitation method | Durable destination | | --- | --- | --- | | Confirm a known rule across several cases | Focused form or case review | Policy, applicability, or rubric contribution | | Discover reasoning that is hard to pre-structure | Chat or interview | Attributable transcript, checkpoints, and contributed artifacts | | Resolve disagreement or ambiguity | Targeted comparison and checkpoint | Explicit unresolved or accepted statement | | Identify missing benchmark behavior | Cases plus coverage questions | Contributed case or coverage observation | | Configure reviewer presentation | Assets → Review Screens | Reusable Review Screen | | Set a contribution's agent behavior | Expert Contribution | Contribution behavior and selected components | ## Why preparation matters An expert should not have to reconstruct the project, search for the controlling source, or infer why a case was selected. Project Context gives the agent the stable brief. Reference Materials provide indexed project knowledge. The Contribution selects the benchmark evidence, states the objective, and configures agent behavior. Review Screen controls presentation. This separation preserves authority. Reference material can inform an answer without becoming a policy. Agent-authored draft wording can focus the expert without becoming approved. A completed expert task can contribute evidence without automatically changing every project artifact. ## Relationship to the product Correctness Elicitation is broader than any single screen. Use **Expert Contributions** to request and track benchmark-scoped expert work. Use **Correctness Governance** to inspect and maintain policies and rubrics after they are materialized. Use **Agent Setup** to configure what agents understand, **Assets → Review Screens** for reusable expert-facing presentation, and the Contribution itself for scoped behavior and components. The capability also returns learning to Coverage Engineering and Weave. An expert may identify a missing behavior combination, contribute a new case, or explain that existing material is insufficient. Those outputs should update the owning coverage or case artifacts instead of being flattened into a general review note. {% example-demo title="Exception handling" %} An agent prepares three cases where a procurement rule might allow an exception, attaches the controlling policy material, and asks a specialist to distinguish approved exceptions from escalation-only situations. The specialist completes a case review and confirms a checkpoint. The accepted contribution materializes a scoped policy and binary rubric, while one unresolved source conflict becomes a coverage observation for follow-up. {% /example-demo %} ## Related workflows {% related-card-grid title="Related workflows" %} - [Request an Expert Contribution](/docs/expert-contributions/request-contribution) - [Manage policies and rubrics](/docs/correctness-governance) - [Configure Agent Setup](/docs/agent-setup) {% /related-card-grid %} ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Human Approval Boundaries](/docs/governance/human-approval-boundaries) - [Policies](/docs/object-model/policies) - [Rubrics](/docs/object-model/rubrics) {% /related-card-grid %} ## Source confidence Doctrine-backed: this page defines the public capability. The linked product pages are code-backed and define the active contribution, governance, and agent-configuration surfaces. --- id: expert-contributions.complete title: Complete an Expert Contribution summary: Work through form, chat, interview, case-review, and checkpoint tasks while keeping specialist judgment attributable. kind: task product_area: expert_contributions status: stable updated: 2026-08-22 canonical: /docs/expert-contributions/complete-contribution --- # Complete an Expert Contribution Complete a Contribution by following its prepared task sequence and making the requested specialist judgments from the evidence shown. The expert experience can adapt between structured forms, agent chat, interviews, case review, and checkpoints. ## Prerequisites - A valid Contribution link or authenticated expert entry point. - Access to the Contribution and its assigned tasks. - Enough source and case context to explain each answer. - A stable connection when the task uses realtime agent interaction. ## Steps 1. Open the Contribution and read its objective, selected cases, and expected components before answering. 2. Complete each task according to its type. Planned activities can be Case Review, Form, Chat, or Interview; Curation, Comparative, and Trajectory components shape the prepared work those activities present. 3. Use attachments and visible case materials as the evidence boundary. State uncertainty when the supplied material does not resolve the question. 4. At a checkpoint, inspect the proposed summary or artifact meaning. Checkpoints prepare and reconcile requirements, consolidator or Policy statements, interview requests or records, and Rubrics. Confirm only what matches your judgment; retry, revise, or leave unresolved anything that does not. 5. Continue through the task handoff until the Contribution reaches its final step. 6. Review the completion state. If the experience shows a waiting, retry, or synchronization state, do not assume the administrator has received final evidence until the product confirms it. ## Object and state changes Answers create durable task responses and can advance task sessions, checkpoints, handoffs, and Contribution status. Chat or interview activity can produce transcripts and structured learning. Case review can attach judgment to selected cases. Completion makes the contribution available for reconciliation and materialization but does not itself make every proposed artifact governed. Review tasks can be `PREPARING`, `BLOCKED`, `READY`, `IN_PROGRESS`, `COMPLETED`, `SKIPPED`, or `SUPERSEDED`. The expert runtime can be `PREPARING`, `READY`, `ACTIVE`, `FINAL_CHECKPOINT`, `COMPLETED`, or `EXHAUSTED`. Checkpoints can be preparing, ready, or reconciled, with individual requirements pending, retryable, materialized, empty, or failed. These layered states explain why a Contribution can be active while one task is blocked or a final checkpoint is still pending. ## Success criteria - Every answer addresses the Contribution objective and cites the visible evidence where needed. - Case-level judgments remain connected to the relevant case. - Checkpoints distinguish accepted, revised, and unresolved meaning. - The final state is visibly complete rather than inferred from navigation. - Uncertainty or source conflict remains explicit for the administrator. ## Common failure modes - Answering from private background without identifying that the supplied evidence is incomplete. - Treating an agent summary as accurate without checking the checkpoint. - Leaving a form or chat task in a local unsynchronized state. - Continuing after a stale task handoff instead of following the current Contribution route. - Assuming that completion directly changes policies, rubrics, cases, or coverage. {% example-demo title="Example: checkpoint correction" %} An interview summary says that every expired agreement should be ignored. The expert corrects the checkpoint: expired agreements may still be relevant when the current agreement explicitly incorporates them. The corrected statement remains attributable and prevents an overbroad policy from being materialized. {% /example-demo %} ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Expert Contributions](/docs/expert-contributions) - [Contributed Artifacts](/docs/expert-contributions/contributed-artifacts) - [Human Approval Boundaries](/docs/governance/human-approval-boundaries) {% /related-card-grid %} ## Related troubleshooting pages {% related-card-grid title="Related troubleshooting pages" %} - [Expert Contribution problems](/docs/troubleshooting/expert-contributions) - [Permissions](/docs/troubleshooting/permissions) - [Authentication](/docs/troubleshooting/authentication) {% /related-card-grid %} ## Source confidence Code-backed: the current expert experience supports form, chat, interview, case-review, checkpoint, completion, waiting, and task-handoff routes with durable command and reconciliation behavior.