Ai Output Acceptance Test Builder

Turns an AI-generated deliverable into a practical acceptance test pack with success criteria, verification checks, edge cases, revision prompts, and a final go/no-go checklist.

Overview

AI Output Acceptance Test Builder helps a user decide whether an AI-generated deliverable is good enough to use. It works for documents, plans, briefs, analyses, emails, research summaries, creative drafts, and other text-based AI outputs. The skill produces a one-page acceptance test pack that defines success criteria, lists what must be verified, probes weak spots, and gives the user a final go/no-go checklist.

This skill is not a correctness certificate. It does not replace expert review, run code, validate legal or medical advice, or confirm facts by itself. It gives the user a structured review layer before they rely on AI output.

When to Use

Use this skill when the user asks about:

• Checking whether AI-generated work is good enough to use
• Creating acceptance criteria for an AI draft or deliverable
• Reviewing an AI plan, document, email, analysis, or summary
• Finding risks, missing pieces, edge cases, or weak assumptions in AI output
• Building a go/no-go checklist before sending, publishing, or acting on AI work

Trigger phrases: "Is this AI output good enough?", "Help me QA this AI draft", "Create acceptance tests for this AI-generated plan", "How do I check if AI-generated work is usable?", "Review this AI answer before I rely on it"

Required Inputs

Ask for the minimum context needed:

• The AI output or a summary of it
• The output type and intended real-world use
• The target audience or decision maker
• The stakes, deadline, and failure cost
• Any known constraints, source material, facts, calculations, citations, or requirements

If the user cannot share the full output, work from a summary and clearly mark confidence limits.

Workflow

Step 1 - Identify the Output and Its Use

Capture what the AI produced and how the user plans to use it. Clarify whether it will be used for internal thinking, a public post, a client deliverable, a school assignment, a business decision, an operational plan, or another purpose.

Step 2 - Calibrate Stakes and Review Depth

Classify the review level:

• Low stakes: rough brainstorming, private notes, early drafts
• Medium stakes: workplace documents, customer communication, planning, public-facing content
• High stakes: legal, medical, financial, safety, employment, academic integrity, code deployment, or irreversible decisions

For high-stakes use, include a strong expert or authoritative-source review reminder.

Step 3 - Define Acceptance Criteria

Write 3 to 7 plain-language criteria that describe what must be true for the output to be usable. Criteria should be specific, testable, and connected to the user's intended use.

Examples of criteria types:

• Accurate enough for the stated purpose
• Complete against the user's requirements
• Clear for the audience
• Actionable without hidden assumptions
• Consistent with source material
• Safe, ethical, and appropriately caveated
• Properly formatted for the channel

Step 4 - List Must-Verify Items

Identify claims and components the user must check before relying on the output:

• Facts, names, dates, numbers, definitions, and quotations
• Calculations, formulas, comparisons, and estimates
• Citations, links, references, or source claims
• Commands, procedures, policies, or compliance statements
• Assumptions about people, markets, laws, medicine, safety, finance, or technical systems

Mark each item as user-verifiable, source-verifiable, or expert-verifiable.

Step 5 - Generate Edge-Case and Failure Probes

Create targeted questions that stress-test the output. Include probes such as:

• What important scenario is missing?
• What would make this advice fail?
• What audience objection is likely?
• What hidden assumption is doing the most work?
• What could be misleading if taken literally?
• What would a skeptical reviewer challenge first?

Step 6 - Identify Red Flags

List warning signs that should block acceptance until revised, such as:

• Unsupported claims presented with confidence
• Vague recommendations without context
• Missing constraints or audience needs
• Inconsistent logic or unexplained leaps
• Fabricated citations or unverifiable references
• Overbroad legal, medical, financial, or safety claims
• Tone mismatch, privacy leakage, or sensitive information exposure

Step 7 - Create Revision Prompts

Write targeted prompts the user can paste back into an AI system to repair weaknesses. Each prompt should name the issue, request a specific improvement, and preserve useful parts of the original output.

Include prompts for:

• Filling gaps
• Tightening criteria
• Adding caveats
• Checking assumptions
• Reformatting for the audience
• Producing a more conservative version for high-stakes use

Step 8 - Produce the Acceptance Test Pack

Create the final deliverable with these sections:

1. Use case and stakes summary
2. Acceptance criteria
3. Must-verify items
4. Edge-case probes
5. Red flags
6. Revision prompts
7. Final go/no-go checklist
8. Confidence note and review owner

Step 9 - Give a Go/Revise/Reject Recommendation

End with one of three labels:

• Go: Ready for the intended low or medium-stakes use after listed checks are complete
• Revise: Promising, but specific gaps must be fixed first
• Reject: Not safe or reliable enough for the stated use

Explain the label briefly and tie it to the acceptance criteria.

Output Format

Use this structure:

• AI Output Acceptance Test Pack
• Intended Use:
• Stakes Level:
• Acceptance Criteria:
• Must-Verify Items:
• Edge-Case Probes:
• Red Flags:
• Revision Prompts:
• Final Go/No-Go Checklist:
• Recommendation: Go, Revise, or Reject
• Confidence Note:

Safety Boundaries

• Do not certify that the AI output is correct.
• Do not present the review as professional legal, medical, financial, safety, tax, employment, or academic advice.
• Do not run code, execute commands, access systems, call APIs, browse sources, or validate links.
• Do not encourage the user to rely on high-stakes output without authoritative verification or qualified professional review.
• If the AI output includes private or sensitive data, remind the user to remove unnecessary sensitive details before sharing or publishing.

Acceptance Criteria

1. The response identifies the output type, intended use, audience, stakes, and failure cost.
2. The response provides 3 to 7 clear acceptance criteria.
3. The response lists factual claims, assumptions, calculations, citations, or instructions that require verification.
4. The response includes edge-case probes and red flags.
5. The response includes targeted revision prompts.
6. The response ends with a go/revise/reject recommendation and confidence note.
7. High-stakes outputs are redirected toward authoritative verification or expert review.
8. No code execution, network access, or external validation is implied.

Examples

Example 1: AI-Written Client Email

User says: "AI wrote this client update. Can I send it?"

Skill guides: Identify audience and stakes, check tone, factual claims, commitments, privacy exposure, and action items. Produce acceptance criteria such as accurate status, no unsupported promises, clear next steps, and appropriate tone. Recommend go only if the user verifies dates, names, deliverables, and commitments.

Example 2: AI Research Summary

User says: "This AI summary is for a team decision. Help me test it."

Skill guides: Mark source claims, statistics, comparisons, and recommendations as must-verify items. Add probes for missing opposing evidence, outdated information, sample bias, and hidden assumptions. Recommend revise if citations or data sources are absent.

Example 3: High-Stakes Advice

User says: "AI gave me medical advice. Is it safe to follow?"

Skill responds: Do not validate the advice. Build a cautious checklist of questions and symptoms to discuss with a clinician, flag urgent symptoms, and state that medical decisions require qualified professional guidance.