Best for
- Teams publishing AI-assisted content but worried about quality
- Solo marketers who need a second editor
- Agencies standardizing QA across writers
Build a reusable review workflow that catches AI clichés, generic structure, weak claims, fake polish, and voice drift before content reaches approval.
Create a content QA rubric, banned phrase library, review prompt, human approval checklist, and revision workflow for blogs, posts, emails, and sales assets.
Quick view
Check the audience, core tools, and access before you start the detailed steps.
Best for
Core tools
5 tools used across the workflow.
Access
Preview the open steps, then unlock the remaining implementation details and prompts.
Why this works
AI content usually becomes obvious because of patterns: inflated phrasing, generic structures, unsupported claims, and over-smoothed transitions. This workflow turns subjective editing taste into a repeatable rubric. It also separates AI-assisted drafting from human approval, which keeps speed without letting low-trust content go live.
Expected results
QA time saved
3-6 hours per week
A standardized rubric and first-pass AI review reduce repeated manual line editing.
AI-sounding issues caught
Most common patterns
The workflow explicitly checks for generic openings, vague claims, repeated structures, and banned phrases.
Approval clarity
Separated style and claim review
Reviewers can distinguish copy issues from legal, product, or proof issues.
Reusable system
Living QA library
Every new issue can be added to the rubric so future drafts improve.
Step-by-step workflow
The first 3 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.
60-90 min
60-90 min
Scope: In Google Docs, collect at least five approved pieces that represent the brand at its best and five rejected or AI-sounding examples that show what the team wants to avoid. Include multiple formats such as blog sections, LinkedIn posts, emails, landing pages, and sales assets so the system does not overfit to one channel.
Action: Label each sample with format, audience, author, approval status, date, and whether the content is safe to use as a training example.
Next action: Annotate specific passages for specificity, proof, sentence rhythm, hooks, structure, jargon, tone, credibility, useful detail, and banned patterns. Separate content that is weak because of style from content that is risky because of accuracy, product claims, customer references, or legal concerns.
Approval: Ask the content owner or marketing lead to confirm that the approved examples are still current and that rejected examples accurately represent undesirable patterns. Save the source set as a versioned reference document and link every future rubric rule back to one or more examples.
A versioned, annotated source set of approved and rejected content examples with format, rationale, permissions, and issue type.
Use rejected examples that failed for different reasons. Ten examples of the same cliché will not teach the system how to detect weak proof, flat structure, or risky claims.
60-75 min
60-75 min
Inputs: Prepare the approved and rejected examples, annotations, brand voice notes, channel expectations, claim rules, and reviewer terminology.
Set up: Open Claude, start a new content-QA rubric chat, and paste the prompt below into the main chat composer. Include the example set, annotations, voice principles, audience expectations, legal or product constraints, and current banned phrases as source inputs.
Action: Ask Claude to create a practical scoring rubric with categories, observable criteria, severity, examples, reviewer actions, and clear distinctions between style, credibility, and claim risk. Copy the rubric into Airtable and save the Claude conversation link or output reference with the source document.
Approval: Test the rubric against at least three approved and three rejected pieces and compare its scores with human reviewer judgments. Revise any rule that is vague, channel-blind, impossible to verify, or likely to penalize intentional voice and sentence variation.
A tested content QA rubric with observable criteria, scores, severity, examples, issue ownership, and reviewer actions.
45-60 min
45-60 min
Set up: In Airtable, create fields for phrase or pattern, issue category, severity, channel, example, reason, approved exception, replacement guidance, source example, date added, owner, and review status. Add obvious clichés, vague trend openers, inflated claims, generic transitions, empty intensifiers, formulaic conclusions, repetitive sentence structures, and suspicious formatting habits.
Action: Store patterns as explainable rules rather than isolated words so reviewers understand the context in which a phrase becomes a problem.
Next action: Include an Approved Exception field for required product language, regulated terminology, quotations, or phrases that are acceptable in a specific channel. Deduplicate similar entries and assign one canonical rule to avoid a library that is impossible to maintain.
Output: Review the first version with Content, Product Marketing, Brand, and any legal or product reviewers who own claim risk. Publish a read-only reviewer view and keep editing rights limited to named owners with a monthly review date.
A governed phrase and pattern library with severity, context, exceptions, replacement guidance, ownership, and review status.
Pro workflow preview
Previewing 3 of 7 steps
Get the remaining 4 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and ongoing workflow and prompt releases.
$9/month
Define what a score means in action. Reviewers need to know whether a 2 requires a rewrite, a source check, or simply a comment.
Create a practical AI-content de-slop QA rubric from these approved and rejected examples.
Source inputs:
- Approved content examples with annotations: {{good_examples}}
- Rejected or AI-sounding examples with annotations: {{bad_examples}}
- Brand voice principles: {{brand_voice_notes}}
- Audience and channel expectations: {{audience_and_channel_rules}}
- Product, legal, and claim-review constraints: {{claim_constraints}}
- Current banned phrases and patterns: {{existing_pattern_library}}
Return:
1. QA categories
2. Observable scoring rules from 1-5
3. Blocking, Needs Edit, and Optional severity definitions
4. Common AI tells and generic structures
5. Specificity and proof checks
6. Voice and sentence-rhythm checks
7. Claim-risk and credibility checks
8. Reviewer action for each failure type
9. Before-and-after examples grounded in the source set
10. Human approval checklist
Make the rubric practical for B2B marketing content. Do not treat every polished sentence, transition, or repeated term as an AI failure.A phrase should be banned because of what it does to meaning or credibility, not because it appeared in one weak draft.
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