AI Task Brief: From Problem to Human Acceptance

Copy this into a private project directory before a model call. It is not a prompt collection. It is permission to start: when task, data, sources, acceptance, or ownership remain unclear, do not ask a model for the final result yet.

Do not include passwords, identity documents, precise addresses, medical privacy, children's data, customer records, unpublished vulnerabilities, or unauthorised third-party material. Redact sensitive material or use an organisation-approved environment.

1. Task and Ownership

# AI Task Brief - YYYY-MM-DD

Real situation:
User/audience:
Decision or action to complete:
Why now:
Deadline and non-negotiable checkpoints:
Final owner and human reviewer:
Who is affected if this is delayed or not done:

Replace “learn AI” or “build an intelligent assistant” with an observable action, such as “a user can import a file in ten minutes and see an explainable report with sources and errors”.

2. Inputs, Sources, and Data Boundary

Input/claim	Type	Source, version, location	May reach model?	Verifier	Expiry condition
	fact / inference / experience / user data / third-party		public / redacted / approved / prohibited

Files and fields allowed:
Material deliberately withheld:
Data sensitivity: public / internal / confidential / restricted
Collection, transfer, retention, and deletion dates:
Required consent obtained:
Copyright, licence, citation, and authorship requirements:
Minimum data scope the model can see:

A source is not automatically true. Each critical claim must return to an original passage, version, data definition, or direct observation. Model links, numbers, and quotations still require human verification.

3. Output and Evaluation Set

Final deliverable:
Format, length, and audience:
Observable definition of done:
Hard gates that must pass:
Acceptable variation:
Content that must not appear:

Build a small, realistic evaluation set instead of only model-friendly examples:

Sample	Input condition	Expected result	Unacceptable result	Actual result	Evidence location
Normal case
Boundary case
Missing/conflicting input
Redacted historical case

Acceptance should answer whether facts are traceable, the action is complete, errors are visible, permissions are correct, and failure can stop. Fluent, fast, or human-like is not an acceptance standard.

4. AI's Working Scope

AI may: ask / classify / propose explanations / give counterexamples / transcribe
        / draft / suggest tests
AI may not: make final factual decisions / invent sources / approve for the owner
             / cross permissions / complete a prohibited exam or application
             / publish or execute automatically
Mode: diagnosis / assistance / candidate generation / batch processing / other
Model, version, region, and call date:
Prompt, system instruction, or workflow version location:

Ask the model to restate goal, input, limits, unknowns, and acceptance before generation. Version prompts, and never treat one chat window as the project's only record.

5. Human Gates

Gate	Who confirms	Passing evidence	If it fails
Source gate		Original link, version, location	Mark unverified; do not circulate
Fact gate		Sample checks and data definition	Delete or downgrade claim
Privacy/permission gate		Scope, consent, access record	Stop and redact
Quality gate		Evaluation set, edge cases, real feedback	Repair, narrow, or reject
Cost gate		Tokens, time, human rework, budget	Downgrade or stop
Ownership gate		Named approval and disclosure	Do not publish or execute

Critical decisions cannot be approved only by the generator, an automated score, or one developer alone. High-risk domains return to current primary sources and qualified professionals.

6. Cost, Retention, and Reversibility

Call volume, time, and cost ceiling:
Human review and rework budget:
Does data enter training, logs, or third-party retention:
Acceptable latency and downgrade path:
Version that can be withdrawn, rerun, or restored:
Release scope and pilot audience:

A cheap call that causes a privacy incident, wrong decision, or major rework is not cheap in reality. Record human time, review, failure, reruns, and communication in addition to model fees.

7. Failure, Pause, and Rollback

Trigger	Immediate action	Notify	Recovery/rollback location
Source or version cannot be found	Stop circulation; return to original
Evaluation hard gate fails	Block release or automatic action
Input crosses permission boundary	Stop upload; revoke access
Cost/latency exceeds ceiling	Downgrade, throttle, or stop
Real user reports harm	Remove, preserve evidence, escalate

Without a named stop owner, notification path, and known-good version, the task is not ready for a real workflow.

8. Handover and Public Disclosure

Current state: not started / experiment / internal pilot / limited release / delivered / stopped
Completed work and evidence locations:
Open questions and risks:
Smallest next task:
Handover owner, date, and access:
What readers/users need to know about AI involvement:
Source, privacy, copyright, and conflict-of-interest note:

The next operator should not search chat history to discover what to do. Public work should state which step used a tool, which facts a person confirmed, and which content remains a candidate.

9. Preflight Check

• [ ] Audience, action, deadline, and owner are explicit.
• [ ] Input fields, sensitivity, consent, copyright, and retention are confirmed.
• [ ] Critical sources, versions, and expiry conditions are traceable.
• [ ] Normal, boundary, conflict, and redacted historical cases are in the evaluation set.
• [ ] AI may/may-not scope is explicit.
• [ ] Human gates, cost ceiling, stop conditions, and rollback location are named.
• [ ] Handover, disclosure, and next review date are set.