Implementation Kit
AI Content Review Checklist and Output Log Template
Controls so AI-generated content meets your accuracy and liability bar before it leaves the building. A content risk-tiering matrix with a review standard per tier, an output log, and a training scenario library of real hallucination cases.
Who this is for: The owner of generative AI use in communications, marketing, legal, or regulatory work.
1. AI content risk tiering matrix
SpreadsheetUse case to risk level to the review standard required before the content is used.
Template
| Use case | Audience / exposure | Risk level | Required review standard | Grounding required |
|---|---|---|---|---|
| <use case> | internal / customer / public / regulator | Low / Moderate / High | scan / verify / independent rewrite | none / RAG with cited sources |
Worked example
| Use case | Exposure | Risk level | Review standard | Grounding |
|---|---|---|---|---|
| Internal meeting summaries | internal | Low | scan for obvious errors | none |
| Marketing copy | public | Moderate | verify all claims, dates, figures | RAG for product facts |
| Customer support replies | customer | Moderate | verify facts; approved-answer check | RAG over the knowledge base |
| Regulatory filing text | regulator | High | independent rewrite by a qualified person; AI draft not used verbatim | RAG with cited primary sources |
| Legal contract clauses | counterparty | High | lawyer drafts; AI for first pass only, every clause checked | none |
Acceptance criteria
- ✓Every generative use case is placed in a tier with a named review standard.
- ✓High-risk uses do not permit AI output to be used verbatim.
- ✓The matrix states where retrieval-grounding with cited sources is mandatory.
2. Review standard definitions
SpreadsheetWhat "scan", "verify", and "independent rewrite" actually require, so the tier label means something.
Template
| Standard | What the reviewer does | Time expectation | Sign-off |
|---|---|---|---|
| Scan | Read for plausibility and obvious errors; fix or reject | minutes | reviewer initials on the log |
| Verify | Check every factual claim, date, figure, name, and citation against a source; annotate each | proportional to claim count | reviewer + source list attached |
| Independent rewrite | Subject-matter expert writes the final version; AI output is reference only | full authoring time | named author owns the content |
Worked example
Customer support reply, Moderate tier, "verify" standard.
| Claim checked | Source | Result |
|---|---|---|
| Refund window | policy doc | correct |
| Product compatibility | spec sheet | correct |
| Named contact | staff directory | correct |
| "48-hour escalation guarantee" | not in policy | hallucinated, removed |
Source list attached to the log; reviewer initials recorded.
Acceptance criteria
- ✓Each standard has a concrete definition a reviewer can follow.
- ✓"Verify" requires a source for each claim, attached to the record.
- ✓Reviewers are trained on which standard applies to their use case.
3. AI-generated content log
SpreadsheetA record for AI content used in significant decisions or external communications.
Template
| Date | Use case | Model / version | Prompt or source summary | Grounding sources | Reviewer | Review standard applied | Changes made | Published / used where |
|---|---|---|---|---|---|---|---|---|
| scan / verify / rewrite |
Worked example
| Date | Use case | Model / version | Prompt summary | Grounding sources | Reviewer | Standard | Changes | Used where |
|---|---|---|---|---|---|---|---|---|
| 2026-09-03 | Support reply | copilot v1.6 | "explain refund eligibility for order X" | KB articles 12, 44 | u-2201 | verify | removed invented 48h guarantee | ticket 88213 |
| 2026-09-04 | Blog post | ext API | "draft 600 words on AI inventory basics" | none | u-1180 | verify | corrected 2 stats, added 3 citations | /blog/ai-inventory-basics |
Acceptance criteria
- ✓Content used externally or in significant decisions is logged with its model version and reviewer.
- ✓The log records what the reviewer changed, so hallucination patterns are visible over time.
- ✓Grounding sources are listed where the use case required them.
4. Training scenario library
DocumentReal hallucination examples with the correct handling, for staff training.
Template
6-10 short scenarios. Each: the AI output, what is wrong, how a trained person should handle it.
Scenario template:
- Context: what the person asked the AI for
- AI output (excerpt):
- The problem: the specific fabrication or error
- Correct handling: what to check, what to change, whether to use it at all
- Rule it illustrates:
Worked example
Scenario 3 (citations):
- Context: analyst asked the AI to "summarize the case law on AI vendor liability with citations".
- AI output: cited "Meridian Corp v. DataForge, 2024" with a plausible summary.
- The problem: the case does not exist. The citation format and reasoning looked correct.
- Correct handling: never use an AI-provided legal citation without pulling the primary source. Here, no source exists, so the paragraph is deleted and flagged to Legal.
- Rule it illustrates: AI output is a draft. Every citation, statute, date, and figure is verified against a primary source before use.
Scenario 5 (confident numbers):
- Context: marketing asked for "our market share vs competitors".
- AI output: gave specific percentages with a confident tone.
- The problem: the model has no access to that data and invented it.
- Correct handling: treat any specific figure the model was not given as fabricated. Pull the real numbers from the analytics team.
- Rule it illustrates: the model cannot know facts it was not given; specificity is not evidence.
Acceptance criteria
- ✓Scenarios are drawn from real incidents or realistic near-misses, not invented.
- ✓Each scenario ends with the rule it teaches.
- ✓The library is used in onboarding and refreshed as new failure patterns appear.
Governance controls this kit produces evidence for
Completing the artifacts above gives you a head start on the evidence requirements for these controls.
The risk matrix, review standards, and log are the hallucination detection and mitigation process.
The "verify" and "rewrite" standards plus the log are the AI output validation record.
The High-tier review standard is pre-publication verification for high-stakes claims.
The content log supports AI-generated deliverable disclosure and citation standards.
The content log extends decision and output logging to generative use.
This kit backs one playbook. Read the full guidance for the reasoning behind each artifact.
Decide what to implement next
Assess your governance gaps, then create an action plan with owners and target dates. Build and export without an account; sign in when you want to save your plan.
Start the AI governance assessment →