JEV: AI decisions
in practice.

Confront a statement with the available evidence.

Suggest the responsible team for a request.

Identify points that require specialized review.

Distinguish a change proposal from a confirmation.

Choose the required capacity before generating.

Assess an explicit criterion of an agent's output.

Dispatch to a specialty without expanding permissions.

Select a candidate from the page text.

Fictional organization study, always supervised.

Organize a fictional queue according to an explicit policy.

Distinguish similar specialties and allow none.

Separate correctness and usefulness before proposing a revision.

Preserve the passage that contradicts the hypothesis.

Observe queue, priority, sufficiency and refund request together.

A conversation can contain sales and scheduling.

Select the excerpt; leave calendar and comparison to code.

Use an operational rubric, without inferring the entire incident.

Classify only fictional material that has already been minimized.

Wi‑Fi can be the subject of a status request.

Flag a weakened test with context and requirement.

12 tasks to choose between rule, Choice, Noul, Score or generation.

Six policies to distinguish refund, credit, conflict and insufficiency.

30 fictional tickets to classify queues and investigate discrepancies.

Eight pairs and four clauses to identify support and omission.

12 tasks to separate selection, permission and attempt limit.

Five candidate lists to choose, abstain, or request review.

Spreadsheet to sum entry, fallback, review and deployment.

Ten simulated predictions to compare coverage and error.

Eight requests to select a skill or none.

Six situations to separate correctness, usefulness and test evidence.

Six passages to preserve contradictions, negations and IDs.

Eight claims to judge the evidence and what is still missing.

Classify tickets to the correct queue.

Insert the evaluation between ticket creation and queue selection.

Qualify opportunities according to explicit criteria.

Trigger after receiving a contact form. Add your catalog and commercial criteria to the context.

Organize post‑sale requests.

Evaluate the ticket with only the strictly necessary order data.

Check whether a text follows the provided brief.

Evaluate the draft before the editorial approval stage.

Triage bug reports for the responsible component.

Use the issue title, description and minimized logs as input.

Choose among registered skills without executing them.

Place the classifier before the tools dispatcher. Use only IDs from the allowed catalog.

Apply an explicit rubric to support the teacher’s review.

Use fictional exercises first; then evaluate minimized responses against the teacher’s rubrics.

Check mandatory information in documents.

Extract text before this step and attach the checklist.

Classify pending items for the responsible person's review.

Use project updates as input before the follow‑up meeting.

Assess whether a provided excerpt supports a claim.

Use after retrieving excerpts from the knowledge base and before drafting a response.

Separate messages and flag those that need review.

After receiving the email in your backend; send only the required body and profile.

Prioritize questions and content suggestions.

After importing comments via your application's authorized integration.

Locate help requests and expressed dissatisfaction.

After a post or question is entered into the learning environment.

Check decision, next step, owner, and deadline.

After the meeting has been authorizedly transcribed.

Assess whether a transcript excerpt supports an independent clip.

After transcribing and segmenting in inemavox; send the text and timestamps, not the video file.

Separate tasks, ideas, journal entries, and references after transcription.

After a textual note or a transcription in your bot; keep the original in the source system.

Prioritize textual relevance according to declared interests.

After importing posts through an authorized mechanism.