From AI Slop to Spin: A Practical QA Checklist for Human-Reviewed Email Copy

Hook: Your inbox is being poisoned — and speed isn’t the fix

AI makes drafting email copy fast, but speed alone doesn’t protect your deliverability or conversion. What does? Structure: rigorous briefs, staged QA gates and required human sign-offs. By 2026, teams that only rely on raw LLM output are seeing the cost — lower engagement, more spam-foldering and regulatory scrutiny. This article turns the popular “3 strategies for killing AI slop” into an operational, step-by-step QA checklist and workflow small teams can implement in days.

Why this matters now (2026 context)

“Slop” was Merriam‑Webster’s 2025 Word of the Year — and not for nothing. AI-generated low-quality content has become a measurable inbox risk. Industry analysts and deliverability experts reported in late 2025 and early 2026 that email messages which read as ‘‘AI-ish’’ can underperform on opens and clicks and trigger stricter ISP filters. As Jay Schwedelson and others observed, AI-sounding language can depress engagement metrics — the primary signal mail providers use to decide inbox placement.

"Un-AI your marketing" — Jay Schwedelson (summary of late‑2025 deliverability observations)

On the regulatory side, AI governance guidance and transparency rules rolled into the broader AI Act implementations in the EU (2025–26) and emerging FTC guidance in the U.S. mean teams must label AI use and retain audit trails. For small teams, this is double pressure: maintain speed while proving quality and compliance.

Executive summary: the operational approach

Convert the three strategy pillars into a repeatable workflow:

1. Brief templates that control prompt inputs and guardrails.
2. QA gates (automated + human) placed before any send.
3. Human sign-offs with explicit responsibilities and audit trails.

Below is a practical checklist, sample brief fields, gate definitions, role matrix and a lightweight workflow you can implement using common tools like Google Docs, Airtable, Slack, Litmus, and Zapier/Make.

Part 1 — Build a content brief that prevents slop

A consistent brief is the single biggest lever. Treat the brief as an engineering input: constrain the model, standardize variables and supply examples.

Essential brief fields (use as a template)

• Campaign name (e.g., Welcome Series - Promo Feb 2026)
• Audience segment (behavioral + list size + suppression rules)
• Goal / KPI (open %, CTR, revenue per send)
• Required CTA (exact link and conversion event)
• Tone & voice (2–3 permitted adjectives + banned phrases)
• Length constraints (characters for subject, sentences for body)
• Mandatory legal copy (disclosures, privacy language, opt-out)
• Personalization tokens (fallback values and limits)
• Deliverability constraints (no more than X links, host domains allowed)
• Examples (1 good example, 1 bad example — show AI-sounding vs. human)
• QA checklist items to be run (deliverability tests, spam score)
• AI provenance (was AI used? which model/version? prompts used)

Practical note: store briefs in a shared Airtable or Google Drive folder. Use a single source of truth and require the brief to be completed before any prompt is executed.

Sample prompt (constrained and few‑shot)

Insert into your prompt engineer’s tool or copy into a template field:

"Produce a 40–60 word marketing email body for Welcome Series - Promo Feb 2026 aimed at new trial users. Tone: concise, helpful, mildly witty; avoid terms: 'innovative', 'cutting-edge', 'disrupt'. Use token {{first_name}}. Include one CTA: 'Activate your plan' with URL https://example.com/activate. Provide both HTML and plain-text versions and a subject line no longer than 50 characters. After generation, run the 'slop filter' (see list) and mark any flagged phrases."

Part 2 — Implement QA gates (automated + human)

Design gates as checkpoints. Each gate either passes the message forward, requires edits, or stops the send. Gates should be automated where possible to preserve speed.

Gate 0 — Pre-generation validation (automated)

• Verify brief completeness. If any mandatory field is empty, block prompt execution.
• Check suppression lists and compliance flags via CRM (GDPR/consent status).

Gate 1 — Automated slop filter and static checks

Run these automated tests immediately after AI generation:

• Phrase match against a maintained AI-sounding phrase list (e.g., 'cutting-edge', 'revolutionary', 'as a leader') — flag or auto-rewrite.
• Spam-score tool (Mail-Tester, Postmark) — threshold spam score < 5.
• Authentication checks (SPF/DKIM/DMARC configured for sending domain).
• Link domain whitelist (ensure all CTA domains are approved).
• Broken-link test and click-tracking present.
• Accessibility check: images have alt text, color contrast clear.

Gate 2 — Deliverability and rendering tests

• Send to a seed list (Gmail, Outlook, Yahoo, mobile clients) via Litmus or Email on Acid.
• Check inbox placement for one or two trusted accounts; adjust subject/preheader if promotional signals are too strong.
• Monitor reply-to and from-name consistency.

Gate 3 — Editorial QA (human)

Editorial reviewers check for brand voice, clarity, and conversion logic. Use a lightweight checklist:

• Subject + preheader alignment — subject delivers on preheader promise.
• One primary CTA; CTAs prioritized and visible.
• No AI-sounding phrases flagged; if present, rewrite with examples from brief.
• Personalization tokens validated; fallbacks defined.
• Length, grammar, and factual accuracy verified.

Gate 4 — Compliance & legal (human/automated)

• Confirm required disclosures and consent lines present.
• Retention of the AI prompt and generated output in an audit log — required for AI governance.
• For regulated verticals (health, finance), route to legal for an explicit check.

Gate 5 — Final human sign-off

One authorized human must sign off before scheduling. Details on sign-off below.

Part 3 — Human sign-offs and the RACI

Small teams win by keeping the sign-off process tight. Use a RACI (Responsible, Accountable, Consulted, Informed) with explicit time SLAs. Keep sign-offs to two layers whenever possible.

Recommended RACI for small teams (example)

• Content creator (R): prepares brief, runs prompt, fixes flagged language.
• Deliverability lead (R): runs seed tests and authentication checks.
• Editor or Marketing Lead (A): final approve for voice & conversion.
• Compliance (C): consulted when regulated content or credit/health claims exist.
• Ops/Owner (I): informed of scheduled sends and performance KPIs.

Sign-off SLA: editor signs within 4 business hours for regular campaigns, 24 hours for regulatory review. If SLA is missed, auto-escalate to a backup signatory.

Audit trail and provenance

• Store the final brief, prompt, model metadata (provider, model version), and final approved copy in a secure folder with timestamped versions.
• Keep a sign-off record (name, timestamp, comments). This supports AI governance and any future dispute or compliance review.

Lightweight workflow you can implement in 2 days

Here’s a compact, practical flow for teams with limited resources. Tools in parentheses are optional but common.

1. Complete brief template in Airtable/Google Form (enforce required fields).
2. Trigger LLM generation via an integration (Zapier/Make or native API), store prompt+output in a Google Doc.
3. Run automated slop filter and spam checks via an API (Mail-Tester, custom regex script).
4. If passed, automatically send to Litmus seed list; results posted to Slack channel.
5. Editor receives Slack message with a link to the draft and the QA results; signs off in the Airtable record.
6. On sign-off, schedule send in ESP (Mailchimp, Klaviyo, SendGrid) and tag the send with the brief ID and model metadata.

Automation reduces friction but keep the human check at Gate 3 and Gate 5 mandatory.

Operational Email QA Checklist (copy-paste)

• Brief completed and stored (ID: _______)
• AI provenance captured (model & prompt saved)
• Subject line: max X chars, personalized token previewed
• Preheader: aligns with subject
• From name & reply-to: consistent
• Primary CTA present and live URL confirmed
• Links: UTM encoded and on whitelist
• Images: alt text present & size optimized
• Plain-text version matches intent
• Spam score: below threshold
• Seed tests: inbox placement checked
• Deliverability/authentication: SPF/DKIM/DMARC pass
• Personalization tokens validated with sample data
• Accessibility basics covered (alt text, readable fonts)
• Legal disclosures present and consent verified
• Editor sign-off (name, timestamp)
• Compliance sign-off if required
• Send scheduled with tracking tags and monitoring plan

Monitoring post-send and continuous improvement

QA doesn’t end at send. Monitor and iterate:

• Primary KPIs: inbox placement, open rate, CTR, conversion rate, complaint rate, unsubscribe.
• Track AI provenance vs. performance. Keep a simple dashboard that compares AI-generated vs. human-edited outputs on these KPIs.
• If AI-origin emails underperform, analyze language features and retrain your prompt library.
• Run periodic A/B tests (1 variable at a time). Document results in the brief library so future models learn what works.

AI governance: lightweight but real

Governance doesn’t have to be a large program. For small teams implement these three minimums:

• Transparency: Tag content as AI-assisted in your audit logs.
• Accountability: Keep sign-off records and model/version metadata.
• Guardrails: Maintain an approved phrase list and forbidden claims list, and update it monthly.

Real-world example (practical, anonymized)

A 10-person ecommerce team implemented the brief + gate workflow in six working days. They enforced brief completion, automated spam/seed tests, and required editorial sign-off. Within eight weeks they saw a measurable improvement: fewer spam-folder reports from seed accounts and an increase in CTR on promotional emails. Their key win was preventing the repetitive, AI-generated hyperbolic language that had previously triggered lower engagement and user complaints. (Anonymized and representative of common outcomes teams report in 2025–26.)

Quick wins you can implement this week

• Create a one-page brief template and make it required for every email.
• Add an automated spam-score check to your generation workflow.
• Start a maintained list of banned AI phrases and run a phrase-match filter.
• Require one explicit editor sign-off before scheduling.

Advanced moves for teams ready to scale

• Build a prompt-and-output repository and use it to fine-tune private models with on-brand examples.
• Automate seed list sends and parse results into an observability dashboard.
• Integrate user engagement signals into content generation prompts (e.g., "Prefer language that historically increases CTR among this segment").

Key takeaways — what to do next

• Stop blaming speed: enforce structure. Briefs are the foundation.
• Gate your output: combine automated filters with mandatory human review.
• Sign-off matters: a single accountable editor improves voice and deliverability.
• Measure and iterate: track AI provenance vs. performance and refine prompts monthly.

Call to action

Ready to stop AI slop and protect your inbox performance? Download the free operational checklist and brief templates we use with small marketing teams, or schedule a 20‑minute workflow review to map this process onto your existing stack. Implement the brief, gate, sign-off loop this week — your next send should be safer and higher performing.

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